Abstract

The hyperspectral data and 3D structural data are highly useful in botanical research. But, the two types of information are often acquired separately and hard to be combined. In this work, a novel dual-path configuration based on acousto-optical tunable filter (AOTF) is proposed to acquire an image, structural and hyperspectral information within one acquisition process by a combination of laser triangulation. Under the configuration, the hyperspectral data and the 3D structure can be matched to subpixel level after geometrical calibration. Finally, the obtainment of 3D hyperspectral information in field experiment verifies the feasibility of this imaging system.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

With the improvement of application demand, the single spectral information or structural information is becoming insufficient. The combination of the two data has been used in many researches [1–3] and provides improved outcomes distinctly over use of either data set alone [4,5]. In botanical research, the acquisition of hyperspectral data and three dimensional (3D) structural information of vegetation in fine scale enables the evaluation of three dimensional radiative transfer modeling [6,7], and validation of remote sensing inversion products, e.g. leaf area index and canopy height [8], which are important in quantitative remote sensing.

Traditional ways to get these data by different instruments separately are very tedious, which often bring complex testing process and heavy workload to the researchers. Furthermore, the different types of data are hardly registered, which make it difficult to establish an accurate model. Thus, the obtainment of unified information including spectral images and 3D structure data has already aroused interests of many researchers in recent years. And for different applications, they designed several ground-based prototypes with various detection methods to obtain the two kinds of data, which can be classified into four types by hyperspectral imaging technologies.

The combination of traditional dispersive scanning spectrometer and light detection and ranging (LIDAR) or laser triangulation is used frequently [9–12], because of the simple structures. There are two scanning models of imaging systems with linear field of view (FOV), i.e. translation-stage-based model and rotating-stage-based model. The both models have tradeoff between scanning time and spatial resolution. Moreover, the detection zone of the former, limited by the route length of translation stage, is often small, which is inappropriate in field measurement. The later have different ground sample distance caused by geometrical distortion of edge FOV.

The holographic interference technique is able to obtain the two data simultaneously without mismatching. K. Yoshimori achieved spectral imaging for three-dimensional objects illuminated by a natural light source based on digital holography technology [13]. It also needs translation stage in two directions to obtain elementally interference patterns and is suitable for using in laboratory. Later, an experimental set up based on Fourier transform was established by C. Zhou et al. for spectral and shape measurement of microstructure [14]. The depth measurement range decided by the moving range of mirror is on micrometer level.

The spectrometers based on filters are also studied. H. D. Liang et al. designed a 3D spectral imaging instrument for large-scale targets (fresco in reference). It has 9 filters to generate spectral images and measures distance of target by the position of focus. The distance accuracy is determined by signal-to-noise ratio (SNR) of images, which decreases with the increasing of spectral resolution [15]. Devoted to cultural heritage monitoring too, the project SYDDARTA [16] developed a 3D-hyperspectral imaging system based on coded structured light and tunable filters that result in high measurement precision and spectral resolution. But, the active light sources, such as digital light projector (DLP) and short-wavelength infrared (SWIR) light source with acousto-optic tunable filter (AOTF), may be inapplicable under the sun. J. Liang et al. acquires 3D plant modeling with AOTF-based spectrometer by multi-angle observation and 3D reconstruction from hyperspectral images, which also works in a controlled lab environment [17].

With the popularity of compressive sensing these years, M. H. Kim et al. realized 3D spectral imaging with high spatial and spectral resolution in wide wavelength range based-on simple combination of the coded aperture snapshot spectral imager (CASSI) and laser scanning system. But, the acquisition and processing times limit the range of applications of this high performance device [18]. Accordingly, inspired by integral imaging, W. Y. Feng et al. replaced the laser scanning system by a microlens array (MLA) for measurement of dynamic scenes [19]. The prototype can capture spectral data of 3D objects in a single snapshot without requiring 3D scanning, but the spatial reconstruction quality declined obviously when compared with Kim’s system.

The systems mentioned above make a great breakthrough in the research and development of this new-type instrument. But, in order to improve its applicability further, the spatial and spectral resolution of images, the registration accuracy between spectral and structural data, the performance in the field and the acquisition time should be concerned synthetically. In this work, we proposed a new 3D hyperspectral imaging system based on AOTF. It can acquire panchromatic images, hyperspectral images and 3D structure data simultaneously and automatically registered on a sub-pixel level. Moreover, the outdoor experiment verifies its field application ability.

2. Theoretical background

2.1 Working principle of AOTF

As shown in Fig. 1, the non-collinear acousto-optic tunable filter is an electronic controlling bandpass filter composed by acousto-optic (AO) crystal, transducer and absorber [20]. When the acoustic wave generated by transducer propagates in the crystal, a phase grating resulting from periodic variation of refractive index is formed. And then, a certain narrow passband of incident light is diffracted, of which the polarization state is rotated by 90° at the same time.

 figure: Fig. 1

Fig. 1 Schematic diagram of non-collinear AOTF.

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According to the acousto-optic Bragg diffraction theory, the central wavelength of diffracted beam has a specific relationship with the frequency of acoustic wave. Thus, the data of different spectral bands can be obtained by tuning the radio frequency (RF).Owing to the staring imaging, the spectrometers based on AOTF is very suitable for terrestrial platform and has been widely used in many fields [21–26].

The acousto-optic interaction of AOTF can be explained by momentum matching theory of acousto-optic vectors. In addition, the non-collinear AOTF often works under the noncritical phase matching (NPM) condition proposed by Chang [27], i.e. the tangents to the light wave vector surfaces are parallel, which results in an AOTF with a large angular aperture. Assuming the incident light is ordinary, Fig. 2 illustrates the vectors relation in AOTF when momentum matching and NPM condition are both satisfied.

 figure: Fig. 2

Fig. 2 Wave vector diagram for the non-collinear AOTF.

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The θi, θa and θd are the polar angles of the incident optical light, diffracted light and acoustic wave respectively. Momentum matching requires that

ki±ka=kd,
where ki is the incident optical wave vector, ka is the acoustic wave vector, and kd is the diffracted optical wave vector. The magnitudes of the wave vectors are given by
ki=2πniλ0,kd=2πndλ0,ka=2πΛ,
where λ0 is the vacuum optical wavelength, and Λ is the acoustic wavelength. The refractive indices of incident light ni and the diffracted light nd can be expressed as
ni=no,
nd=[cos2(θd)/no2+sin2(θd)/ne2]1/2.
no and ne are the ordinary and extraordinary refractive indices in the direction perpendicular to the optical axis, respectively, and can be calculated from Sellmeier dispersion formula [28].

2.2 Ray tracing of AOTF

For an imaging system, the accurate ray tracing is significant in optical design, simulation and optimization. The mechanism of AO interaction is different from other common optical elements, as shown in Fig. 3.

 figure: Fig. 3

Fig. 3 Schematic diagram of light propagation for the non-collinear AOTF.

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The AO crystal changes the direction of incident light by incident surface, AO interaction and exit surface. For undiffracted light, the AO interaction is ineffective, which means the AOTF can be seen as an optical wedge and the direction of exit light can be simply traced by refraction law. With regard to diffracted light, the AO interaction should be considered and the direction of light propagation can be calculated by geometrical relation of wave vectors, the corresponding optical model for the ray tracing of AOTF is proposed in previous work to promote optical design accuracy [29,30].

2.3 Principle of laser triangulation ranging

Triangulation based optical metrology is proper to measure the vegetation canopy structure [31]. The measurement system is composed of laser, oscillating mirrors and camera. The laser is used as the active illumination source. The dual-oscillating mirror module with two orthogonal mirrors is used to turn the laser beam in the two directions (i.e. azimuth and elevation) forming a two-dimensional scan. The CCD camera is used to capture the image of the laser spot, from which the coordinates of the laser spot can be calculated by triangulation [32].

As shown in Fig. 4, the world coordinate system O-XYZ coincides with the camera coordinate system O-XCYCZC. XC-axis and YC-axis are parallel to the u-axis and v-axis of the image plane coordinate system respectively. ZC-axis coincides with the optical axis of the camera. When using perspective camera model, the relationship between the coordinates of the measured point P(xc,yc,zc) and its image plane coordinates P(u,v) is

zc[uv1]=[fx0u00fyv0001][xcyxzc]A[xcyxzc],
where fx, fy, u0 and v0 are the intrinsic parameters of the camera. From Eq. (5), the equation of the chief ray l1 and incident light l2 should be
xcfy(uu0)=ycfx(vv0)=zcfxfy,
xcx2a2=ycy2b2=zcz2c2,
where (x2,y2,z2) is the intersection of the incident beam and the surface of Y-mirror, (a2,b2,c2) is the direction vector of l2.

 figure: Fig. 4

Fig. 4 Simplified schematic diagram of laser triangulation system.

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Due to the location error for the laser spot, the line l1 and line l2 might be skew lines. In this case, the midpoint of the common perpendicular between l1 and line l2 can be treated as the measured point. Assume that

xcfy(uu0)=ycfx(vv0)=zcfxfy=s,xcx2a2=ycy2b2=zcz2c2=t.
Then, p1 and p2, the intersections of the common perpendicular with the l1 and line l2, can be given by
{p1=(sfy(uu0),sfx(vv0),sfxfy)p2=(x2+ta2,y2+tb2,z2+tc2).
The coordinates of the point P(xc,yc,zc) is

xc=sfy(uu0)+x2+ta22,yc=sfx(vv0)+y2+tb22,zc=sfxfy+z2+tc22.

Before measurement, the intrinsic parameters of the camera, (fx,fy,u0,v0), and the parameters of the incident light equation, (x2,y2,z2) and (a2,b2,c2), should be calibrated.

3. Prototype design

3.1 Configuration of 3D hyperspectral imaging system

The AOTF spectrometer and laser triangulation ranging mentioned above are both imaging systems. In typical AOTF spectrometers, the undiffracted beam is considered as stray light and eliminated by polarizer. However, in the proposed imaging system as shown in Fig. 5, the undiffracted beam is separated spatially from diffracted beam by a polarization beam splitter (PBS) because of the orthogonal polarization, and forming panchromatic optical channel to provide high SNR image information of the objects. The imaging detector for the undiffracted beam is coupled with a laser and a dual-oscillating mirror in the meantime to constitute a 3D measurement system. Thus, the imaging system is capable of detecting panchromatic image, hyperspectral data cube and 3D point cloud.

 figure: Fig. 5

Fig. 5 Schematic diagram of the 3D hyperspectral imaging system based on AOTF.

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Owing to the high radiation, the laser beam will not be flooded by the sunlight, so that this device can be used both indoors and outdoors. Also, it takes advantage of AOTF for its high spectral resolution (a few nanometers), fast electronic tuning (tens of nanoseconds) and short acquisition time (a few minutes for hundreds of bands) to prevent excessive change of solar illumination in in situ detection.

Furthermore, the registration of the two kinds of data for this system is equivalent to the image matching for the two channels. The diffracted beam channel and undiffracted beam channel of the imaging system form a common optical path configuration and have the same FOV as well as the same view angle. So, there is no parallax on the measured object and the geometrical relation between spectral images and laser spot images are fixed, which means that the registration is easy to implement. Additionally, it also avoids the mutual occlusion of objects with complex spatial structure, such as leaves, when the spectrometer and the 3D measurement system observe at different view angles.

3.2 Optical design and prototype

Different from normal AOTF-based spectrometers, the geometrical consistency between images of the two optical channels should be concerned about for promotion of registration accuracy. The optical design of the imaging system and analysis of image property adopts ZEMAX and optical model of AOTF introduced in section 2.2. Generally, there are two optical structures used in AOTF imaging spectrometers [33,34], i.e. collimated optics and confocal optics. In order to evaluate the influence of optical model on geometrical consistency, the two structures is simulated in software with the same FOV and entrance pupil, as shown in Fig. 6.

 figure: Fig. 6

Fig. 6 Optical structures of AOTF system: (a) Collimated optics; (b) Confocal optics. In this type of spectrometers, the Etendue is determined by the linear and angular apertures of AOTF. Commonly, in collimated optics, the aperture stop is usually on the surface of crystal and equal to linear aperture, and the angle of incidence should be equal to angular aperture to make maximum Etendue. In confocal optics, on the other hand, the image plane is located at AOTF exit surface and equal to linear aperture, the aperture stop is positioned at focal point of fore optics to ensure the incident angle equal to angular aperture by telecentric structure.

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By the function of grid distortion in ZEMAX, the image maximum distortion of diffracted beam and undffiracted beam in each optical structure can be obtained, as depicted in Fig. 7. Affected by optical wedge of rear surface and exit angle of light, the image planes must be tilted to guarantee the imaging quality over the whole field range in confocal optics. Hence, it is obvious that the distortion in collimated optics is smaller than it in confocal optics, and more importantly, the difference of maximum distortion between optical channels in collimated optics is smaller, which is 1.3771% and 0.2716% respectively. Due to the influence of AOTF is performed in AO interaction direction, the distortion mainly exists in one direction accordingly. So, for a CCD camera with 512 × 512 pixels, the maximum of difference in the two structures can be calculated as 1.3771% × 512/2≈3.53 pixels and 0.2716% × 512/2≈0.7 pixels. Finally, the collimated structure is more conformed to our system.

 figure: Fig. 7

Fig. 7 Partial enlarged drawing of grid distortion: (a) Undiffracted channel in confocal optics; (b) Diffracted channel in confocal optics; (c) Undiffracted channel in collimated optics; (d) Diffracted channel in collimated optics.

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The layout of imaging system and prototype are shown in Fig. 8. The fore optics compresses incident light by 1/3 × and has a diagonal FOV of 15°. The afocal telescope adopts Galileo configuration in order to reduce system size. The doublet prism is used to compensate chromatic aberration and decrease image shift caused by AOTF [35]. The characteristics of prototype are summarized in Table 1 and some indexes are illustrated in next section.

 figure: Fig. 8

Fig. 8 Imaging system design: (a) Prototype; (b) Layout. The instrument has compact structure and is convenient for field experiments. The external dimension is 335mm × 260mm × 130mm.

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Tables Icon

Table 1. Characteristics of the imaging system

4. Experimental results and discussions

4.1 Spectral detection test

An Epipremnum aureum illuminated by a halogen lamp is detected for spectral test in laboratory, as shown in Fig. 9. The spectral data is acquired by the prototype and an Analytical Spectral Devices (ASD) FieldSpec pro FR spectroradiometer (non-imaging system with 25° FOV) under the same lighting condition and the reflectivity is calibrated against a white reference panel. The mean spectral data of the red rectangle area in Fig. 9(b) obtained by the prototype is used to compare with the counterpart obtained by ASD, of which the detection region is represented by the red circle.

 figure: Fig. 9

Fig. 9 Spectral detection test. (a) The test scene; (b) The spectral image at 715nm obtained by prototype.

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The ASD is a high performance device so that it is usually regarded as a standard. Figure 10(a) illustrates that the spectral curves have extreme similarity, which means the prototype is capable of detecting spectrum accurately. The absolute error and relative error can be calculated by

Aerror=RPrototye(λ)RASD(λ),Rerror=RPrototye(λ)RASD(λ)RASD(λ)×100%.
Where λ is wavelength, RASD(λ) is the spectral reflectivity detected by ASD, RPrototype(λ) is the spectral reflectivity detected by prototype. As depicted in Fig. 10(b), the maximum absolute error and relative error between the two curves appear in the short wave end due to the low SNR of spectral images resulting from the low diffraction efficiency of AOTF and low quantum efficiency of CCD camera in these bands, which declines reflectivity inversion precision. Moreover, the plant have strongly absorptive property under 450nm because of chlorophyll, i.e. RASD(λ) is small, thus, a little change will lead to big relative deviation compared with the other bands, which makes the relative error more serious.

 figure: Fig. 10

Fig. 10 Spectral reflectivity of leaves on region of interest obtained by ASD and prototype. (a) The spectral curves; (b) Absolute error and relative error.

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4.2 3D measurement test

The optical structure of this imaging system is more complex than a single laser triangulation system simply composed by lens and camera. Therefore, instead of traditional pinhole model, a multi-plane camera calibration model based on phase fringe and back propagation neural network is proposed in a previous work [36]. The 3D measurement precision is tested by planar board and standard sample.

A standard planar board overlaying entire FOV is measured at a fixed position, and the plane is fitted by point cloud. Figure 11(b) exhibits the residuals of the plane fitting, the standard deviation is 0.48 mm which reflects the plane measurement error of the prototype.

 figure: Fig. 11

Fig. 11 Plane fitting test: (a) The test scene; (b) Residuals of the plane fitting.

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The 3D measurement precision is tested by a standard stepped sample with fixed distance of 9.02 mm between working surface 1 and 2, shown in Fig. 12(a). After retrieving point cloud of the sample, the plane equation of working surface 1 can be acquired by plane fitting. And then, the distances between points of working surface 2 and the fitted plane can be calculated, of which the mean value represents the measured distance between the working surfaces. The measured result is 9.91 mm and the measurement error is 0.89 mm.

 figure: Fig. 12

Fig. 12 3D measurement test: (a) The standard stepped sample; (b) Point cloud; (c) Method.

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Obviously, the 3D measurement accuracy is not as good as traditional laser triangulation devices, mainly because of the short baseline for compact structure of the prototype, as shown in Fig. 8(a). Meanwhile, for vegetation observation at the close range and the application requirements can be satisfied by the current measurement accuracy.

4.3 Unified geometrical calibration for the two channels

The geometrical property of diffracted image and undiffracted image should be similar as analyzed in section 3.2, but there are still several factors exiting in the prototype resulting in decline of geometrical consistency between two channels:

  • • The collimated incident light produced by afocal telescope is imperfect;
  • • The two optical channels are different in the aspects of wavelength range and detector size, so the doublet prisms and imaging lenses used have different parameters in order to correct chromatic aberrations and ensure the image quality;
  • • The finite precision of optical design, element manufacturing, assembly, etc.

In order to promote registration accuracy, the checkerboard target is used in unified geometrical calibration for the two channels. First, the target is imaged by the two optical channels simultaneously, as shown in Fig. 13, and every corner point is retrieved on a subpixel level, emphasized by green circles in Fig. 13. Then, according to the coordinate transformation relationship established by the corresponding points, the spectral image is resampled to register with panchromatic image. The effect of the registration is depicted in Fig. 14, several more feature points are extracted by using SIFT operator on a subpixel level [37] for evaluation of the registration accuracy. After that, the RANSAC algorithm [38] removes the mismatch points, and the root-mean-square error of registration is calculated as:

errorRMS=i=1k[(xuxd)2+(yuyd)2]k=0.63pixels.
where (xu,yu) and (xd,yd) represent the coordinate of the corresponding points in undiffracted image and diffracted image respectively, k is the number of the corresponding points.

 figure: Fig. 13

Fig. 13 Images of checkerboard target: (a) Undiffracted channel; (b) Diffracted channel.

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 figure: Fig. 14

Fig. 14 Registration accuracy measurement: (a) Feature point extraction by SIFT; (b) Removal of mismatching points by RANSAC.

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4.4 Field 3D-hyperspectral detection experiment

In order to verify the practicability of the 3D hyperspectral imaging system, the outdoor experiment is performed with solar elevation of 65° approximately and a potted plant is chosen to be detected from a distance of 1.5m, as shown in Fig. 15(a). According to the design objective of this system, it can be seen from Fig. 15(b) and 15(c) that the hyperspectral image and panchromatic image have high geometrical similarity, and the laser point can be detected and extracted from the panchromatic image which makes the system can be used in daylight.

 figure: Fig. 15

Fig. 15 A potted plant is detected by 3D hyperspectral imaging system at noon. (a) The outdoor experiment scene; (b) The spectral image in the band of 563 nm; (c) The panchromatic image of target with laser scanning points.

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Figure 16 exhibits the unified information of the potted plant. The 3D structure is in the top right, the green points represent plant canopy and the red is flowerpot. Because of accurate transformation relationship between two channels, the spectral curve can be flexibly acquired from a single point (blue point) or certain area (red square) of the plant. Also, the panchromatic image with high quality is useful in texture observation. The withered area emphasized in Fig. 15(c) with a green circle can be easily recognized. The spectral reflectivity differs from healthy area distinctly.

 figure: Fig. 16

Fig. 16 The unified information includes panchromatic image, 3D structure and spectral reflectance.

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5. Conclusion

In this work, we have proposed a ground-based imaging system which can acquire unified panchromatic image, hyperspectral data and 3D structure data in field measurement. The system is based on AOTF and laser triangulation, the diffracted beam and undiffracted beam are imaged simultaneously and used for spectral detection and 3D measurement respectively. This common optical path configuration provides the two optical channels with the same FOV and view angle, in which the collimated optics is adopted based on geometrical consistency analysis according to an accurate AOTF model. The results illustrate that the collimated optics is proper to this system. The tests and field experiment demonstrate the feasibility of outdoor 3D-hyperspectral data acquirement, and by simple calibration, the registration error between the two types of data can be controlled to subpixel level (0.63 pixels). This novel and comprehensive data set is helpful to researchers in future 3D radiative transfer modeling of plant.

Funding

National Natural Science Foundation of China (NSFC) (61227806); National Key Research and Development Program of China (2016YFB0500505); National High Technology Research and Development Program (2016YFF0103604).

Acknowledgments

We would like to thank Hongzhi Jiang, Shaoguang Shi and Zufu Xu for useful discussions.

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23. J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005). [CrossRef]  

24. N. Gupta, “Acousto-optic-tunable-filter-based spectropolarimetric imagers for medical diagnostic applications-instrument design point of view,” J. Biomed. Opt. 10(5), 051802 (2005). [CrossRef]   [PubMed]  

25. D. A. Glenar, D. L. Blaney, and J. J. Hillman, “AIMS: acousto-optic imaging spectrometer for spectral mapping of solid surfaces,” Acta Astronaut. 52(2-6), 389–396 (2003). [CrossRef]  

26. Z. P. He, B. Y. Wang, G. Lü, C. L. Li, L. Y. Yuan, R. Xu, B. Liu, K. Chen, and J. Y. Wang, “Operating principles and detection characteristics of the visible and near-infrared imaging spectrometer in the Chang’e-3,” Res. Astron. Astrophys. 14(12), 1567–1577 (2014). [CrossRef]  

27. I. C. Chang, “Non-collinear acousto-optic filter with large angular aperture,” Appl. Phys. Lett. 25(7), 370–372 (1974). [CrossRef]  

28. G. Georgiev, D. A. Glenar, and J. J. Hillman, “Spectral characterization of acousto-optic filters used in imaging spectroscopy,” Appl. Opt. 41(1), 209–217 (2002). [CrossRef]   [PubMed]  

29. P. W. Zhou, H. J. Zhao, Y. Zhang, and C. C. Li, “Accurate optical design of an acousto-optic tunable filter imaging spectrometer,” in Proceedings of IEEE International Conference on Imaging System and Techniques (IEEE, 2012), pp. 249–253. [CrossRef]  

30. H. Zhao, C. Li, and Y. Zhang, “Three-surface model for the ray tracing of an imaging acousto-optic tunable filter,” Appl. Opt. 53(32), 7684–7690 (2014). [CrossRef]   [PubMed]  

31. T. Tanaka, H. Park, and S. Hattori, “Measurement of forest canopy structure by a laser plane range-finding method: improvement of radiative resolution and examples of its application,” Agric. For. Meteorol. 125(1-2), 129–142 (2004). [CrossRef]  

32. X. D. Li, H. J. Zhao, Y. Liu, H. J. Jiang, and Y. Bian, “Laser scanning based three dimensional measurement of vegetation canopy structure,” Opt. Lasers Eng. 54, 152–158 (2014). [CrossRef]  

33. L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995). [CrossRef]  

34. D. R. Suhre, L. J. Denes, and N. Gupta, “Telecentric confocal optics for aberration correction of acousto-optic tunable filters,” Appl. Opt. 43(6), 1255–1260 (2004). [CrossRef]   [PubMed]  

35. H. J. Zhao, P. W. Zhou, Y. Zhang, and C. C. Li, “Lateral chromatic aberrations correction for AOTF imaging spectrometer based on doublet prism,” Guangpuxue Yu Guangpu Fenxi 33(10), 2869–2874 (2013). [PubMed]  

36. H. Zhao, S. Shi, H. Jiang, Y. Zhang, and Z. Xu, “Calibration of AOTF-based 3D measurement system using multiplane model based on phase fringe and BP neural network,” Opt. Express 25(9), 10413–10433 (2017). [CrossRef]   [PubMed]  

37. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004). [CrossRef]  

38. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981). [CrossRef]  

References

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  1. H. Aasen, A. Burkart, A. Bolten, and G. Bareth, “Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance,” ISPRS J. Photogramm. Remote Sens. 108(5), 245–259 (2015).
    [Crossref]
  2. J. Geipel, J. Link, and W. Claupein, “Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system,” Remote Sens. 6(11), 10335–10355 (2014).
    [Crossref]
  3. J. Gastellu-Etchegorry, T. G. Yin, T. Cajgfinger, and E. Grau, “Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes,” Remote Sens. 7(2), 1667–1701 (2015).
    [Crossref]
  4. J. E. Anderson, L. C. Plourde, M. E. Martin, B. H. Braswell, M. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
    [Crossref]
  5. E. Ivorra, S. Verdu, A. J. Sánchez, R. Grau, and J. M. Barat, “Predicting gilthead sea bream (sparus aurata) freshness by a novel combined technique of 3D imaging and SW-NIR spectral analysis,” Sensors (Basel) 16(10), 1735 (2016).
    [Crossref] [PubMed]
  6. F. Zhao, Y. G. Li, X. Dai, W. Verhoef, Y. Q. Guo, H. Shang, X. F. Gu, Y. B. Huang, T. Yu, and J. X. Huang, “Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops,” Remote Sens. Environ. 156, 129–142 (2015).
    [Crossref]
  7. J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
    [Crossref]
  8. S. L. Liang, X. Zhao, S. H. Liu, W. P. Yuan, X. Cheng, Z. Q. Xiao, X. T. Zhang, Q. Liu, J. Cheng, H. R. Tang, T. H. Qu, Y. C. Bo, Y. Qu, H. Z. Ren, K. Yu, and J. Townshend, “A long-term global land surface satellite (GLASS) data-set for environmental studies,” Int. J. Digit. Earth 6(sup1), 5–33 (2013).
    [Crossref]
  9. M. A. Powers and C. C. Davis, “Spectral LADAR: active range-resolved three-dimensional imaging spectroscopy,” Appl. Opt. 51(10), 1468–1478 (2012).
    [Crossref] [PubMed]
  10. N. Brusco, S. Capeleto, M. Fedel, A. Paviotti, L. Poletto, G. M. Cortelazzo, and G. Tondello, “A system for 3D modeling frescoed historical buildings with multispectral texture information,” Mach. Vis. Appl. 17(6), 373–393 (2006).
    [Crossref]
  11. J. Behmann, A. Mahlein, S. Paulus, J. Dupuius, H. kuhlmann, E. Oerke, and L. Plumer, “Generation and application of hyperspectral 3D plant models: methods and challenges,” Mach. Vis. Appl. 27(5), 611–624 (2016).
    [Crossref]
  12. H. J. Zhao, S. G. Shi, X. F. Gu, G. R. Jia, and L. B. Xu, “Integrated system for auto-registered hyperspectral and 3D structure measurement at the point scale,” Remote Sens. 9(6), 512 (2017).
    [Crossref]
  13. K. Yoshimori, “Interferometric spectral imaging for three-dimensional objects illuminated by a natural light source,” J. Opt. Soc. Am. A 18(4), 765 (2001).
    [Crossref]
  14. C. Zhou, H. Wang, Y. Li, and X. H. Bai, “There-dimensional surface full color imaging of microstructure by fourier transform,” Guangdian Gongcheng 39(2), 74–80 (2012).
  15. H. D. Liang, A. Lucian, R. Lange, C. S. Cheung, and B. Su, “Remote spectral imaging with simultaneous extraction of 3D topography for historical wall paintings,” ISPRS J. Photogramm. Remote Sens. 95(3), 13–22 (2014).
    [Crossref]
  16. L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
    [Crossref]
  17. J. Liang, A. Zia, J. Zhou, and X. Sirault, “3D plant modelling via hyperspectral imaging,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2013), pp. 172–177.
  18. M. H. Kim, T. A. Harvey, D. S. Kittle, H. Rushmeier, J. Dorsey, R. O. Prum, and B. J. Javidi, “3D imaging spectroscopy for measuring hyperspectral patterns on solid objects,” ACM Trans. Graph. 31(4), 13–15 (2012).
    [Crossref]
  19. W. Feng, H. Rueda, C. Fu, G. R. Arce, W. He, and Q. Chen, “3D compressive spectral integral imaging,” Opt. Express 24(22), 24859–24871 (2016).
    [Crossref] [PubMed]
  20. I. C. Chang, “Acoustooptic devices and applications,” IEEE Trans. Sonics Ultrason. 23(1), 2–21 (1976).
    [Crossref]
  21. V. Alchanatis, L. Ridel, A. Hetzroni, and L. Yaroslavsky, “Weed detection in multi-spectral images of cotton fields,” Comput. Electron. Agric. 47(3), 243–260 (2005).
    [Crossref]
  22. D. A. Glenar, J. J. Hillman, B. Saif, and J. Bergstralh, “Acousto-optic imaging spectropolarimetry for remote sensing,” Appl. Opt. 33(31), 7412–7424 (1994).
    [Crossref] [PubMed]
  23. J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
    [Crossref]
  24. N. Gupta, “Acousto-optic-tunable-filter-based spectropolarimetric imagers for medical diagnostic applications-instrument design point of view,” J. Biomed. Opt. 10(5), 051802 (2005).
    [Crossref] [PubMed]
  25. D. A. Glenar, D. L. Blaney, and J. J. Hillman, “AIMS: acousto-optic imaging spectrometer for spectral mapping of solid surfaces,” Acta Astronaut. 52(2-6), 389–396 (2003).
    [Crossref]
  26. Z. P. He, B. Y. Wang, G. Lü, C. L. Li, L. Y. Yuan, R. Xu, B. Liu, K. Chen, and J. Y. Wang, “Operating principles and detection characteristics of the visible and near-infrared imaging spectrometer in the Chang’e-3,” Res. Astron. Astrophys. 14(12), 1567–1577 (2014).
    [Crossref]
  27. I. C. Chang, “Non-collinear acousto-optic filter with large angular aperture,” Appl. Phys. Lett. 25(7), 370–372 (1974).
    [Crossref]
  28. G. Georgiev, D. A. Glenar, and J. J. Hillman, “Spectral characterization of acousto-optic filters used in imaging spectroscopy,” Appl. Opt. 41(1), 209–217 (2002).
    [Crossref] [PubMed]
  29. P. W. Zhou, H. J. Zhao, Y. Zhang, and C. C. Li, “Accurate optical design of an acousto-optic tunable filter imaging spectrometer,” in Proceedings of IEEE International Conference on Imaging System and Techniques (IEEE, 2012), pp. 249–253.
    [Crossref]
  30. H. Zhao, C. Li, and Y. Zhang, “Three-surface model for the ray tracing of an imaging acousto-optic tunable filter,” Appl. Opt. 53(32), 7684–7690 (2014).
    [Crossref] [PubMed]
  31. T. Tanaka, H. Park, and S. Hattori, “Measurement of forest canopy structure by a laser plane range-finding method: improvement of radiative resolution and examples of its application,” Agric. For. Meteorol. 125(1-2), 129–142 (2004).
    [Crossref]
  32. X. D. Li, H. J. Zhao, Y. Liu, H. J. Jiang, and Y. Bian, “Laser scanning based three dimensional measurement of vegetation canopy structure,” Opt. Lasers Eng. 54, 152–158 (2014).
    [Crossref]
  33. L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995).
    [Crossref]
  34. D. R. Suhre, L. J. Denes, and N. Gupta, “Telecentric confocal optics for aberration correction of acousto-optic tunable filters,” Appl. Opt. 43(6), 1255–1260 (2004).
    [Crossref] [PubMed]
  35. H. J. Zhao, P. W. Zhou, Y. Zhang, and C. C. Li, “Lateral chromatic aberrations correction for AOTF imaging spectrometer based on doublet prism,” Guangpuxue Yu Guangpu Fenxi 33(10), 2869–2874 (2013).
    [PubMed]
  36. H. Zhao, S. Shi, H. Jiang, Y. Zhang, and Z. Xu, “Calibration of AOTF-based 3D measurement system using multiplane model based on phase fringe and BP neural network,” Opt. Express 25(9), 10413–10433 (2017).
    [Crossref] [PubMed]
  37. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
    [Crossref]
  38. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
    [Crossref]

2017 (2)

H. J. Zhao, S. G. Shi, X. F. Gu, G. R. Jia, and L. B. Xu, “Integrated system for auto-registered hyperspectral and 3D structure measurement at the point scale,” Remote Sens. 9(6), 512 (2017).
[Crossref]

H. Zhao, S. Shi, H. Jiang, Y. Zhang, and Z. Xu, “Calibration of AOTF-based 3D measurement system using multiplane model based on phase fringe and BP neural network,” Opt. Express 25(9), 10413–10433 (2017).
[Crossref] [PubMed]

2016 (3)

J. Behmann, A. Mahlein, S. Paulus, J. Dupuius, H. kuhlmann, E. Oerke, and L. Plumer, “Generation and application of hyperspectral 3D plant models: methods and challenges,” Mach. Vis. Appl. 27(5), 611–624 (2016).
[Crossref]

W. Feng, H. Rueda, C. Fu, G. R. Arce, W. He, and Q. Chen, “3D compressive spectral integral imaging,” Opt. Express 24(22), 24859–24871 (2016).
[Crossref] [PubMed]

E. Ivorra, S. Verdu, A. J. Sánchez, R. Grau, and J. M. Barat, “Predicting gilthead sea bream (sparus aurata) freshness by a novel combined technique of 3D imaging and SW-NIR spectral analysis,” Sensors (Basel) 16(10), 1735 (2016).
[Crossref] [PubMed]

2015 (4)

F. Zhao, Y. G. Li, X. Dai, W. Verhoef, Y. Q. Guo, H. Shang, X. F. Gu, Y. B. Huang, T. Yu, and J. X. Huang, “Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops,” Remote Sens. Environ. 156, 129–142 (2015).
[Crossref]

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
[Crossref]

H. Aasen, A. Burkart, A. Bolten, and G. Bareth, “Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance,” ISPRS J. Photogramm. Remote Sens. 108(5), 245–259 (2015).
[Crossref]

J. Gastellu-Etchegorry, T. G. Yin, T. Cajgfinger, and E. Grau, “Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes,” Remote Sens. 7(2), 1667–1701 (2015).
[Crossref]

2014 (5)

J. Geipel, J. Link, and W. Claupein, “Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system,” Remote Sens. 6(11), 10335–10355 (2014).
[Crossref]

H. D. Liang, A. Lucian, R. Lange, C. S. Cheung, and B. Su, “Remote spectral imaging with simultaneous extraction of 3D topography for historical wall paintings,” ISPRS J. Photogramm. Remote Sens. 95(3), 13–22 (2014).
[Crossref]

X. D. Li, H. J. Zhao, Y. Liu, H. J. Jiang, and Y. Bian, “Laser scanning based three dimensional measurement of vegetation canopy structure,” Opt. Lasers Eng. 54, 152–158 (2014).
[Crossref]

Z. P. He, B. Y. Wang, G. Lü, C. L. Li, L. Y. Yuan, R. Xu, B. Liu, K. Chen, and J. Y. Wang, “Operating principles and detection characteristics of the visible and near-infrared imaging spectrometer in the Chang’e-3,” Res. Astron. Astrophys. 14(12), 1567–1577 (2014).
[Crossref]

H. Zhao, C. Li, and Y. Zhang, “Three-surface model for the ray tracing of an imaging acousto-optic tunable filter,” Appl. Opt. 53(32), 7684–7690 (2014).
[Crossref] [PubMed]

2013 (3)

H. J. Zhao, P. W. Zhou, Y. Zhang, and C. C. Li, “Lateral chromatic aberrations correction for AOTF imaging spectrometer based on doublet prism,” Guangpuxue Yu Guangpu Fenxi 33(10), 2869–2874 (2013).
[PubMed]

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
[Crossref]

S. L. Liang, X. Zhao, S. H. Liu, W. P. Yuan, X. Cheng, Z. Q. Xiao, X. T. Zhang, Q. Liu, J. Cheng, H. R. Tang, T. H. Qu, Y. C. Bo, Y. Qu, H. Z. Ren, K. Yu, and J. Townshend, “A long-term global land surface satellite (GLASS) data-set for environmental studies,” Int. J. Digit. Earth 6(sup1), 5–33 (2013).
[Crossref]

2012 (3)

M. A. Powers and C. C. Davis, “Spectral LADAR: active range-resolved three-dimensional imaging spectroscopy,” Appl. Opt. 51(10), 1468–1478 (2012).
[Crossref] [PubMed]

M. H. Kim, T. A. Harvey, D. S. Kittle, H. Rushmeier, J. Dorsey, R. O. Prum, and B. J. Javidi, “3D imaging spectroscopy for measuring hyperspectral patterns on solid objects,” ACM Trans. Graph. 31(4), 13–15 (2012).
[Crossref]

C. Zhou, H. Wang, Y. Li, and X. H. Bai, “There-dimensional surface full color imaging of microstructure by fourier transform,” Guangdian Gongcheng 39(2), 74–80 (2012).

2008 (1)

J. E. Anderson, L. C. Plourde, M. E. Martin, B. H. Braswell, M. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

2006 (1)

N. Brusco, S. Capeleto, M. Fedel, A. Paviotti, L. Poletto, G. M. Cortelazzo, and G. Tondello, “A system for 3D modeling frescoed historical buildings with multispectral texture information,” Mach. Vis. Appl. 17(6), 373–393 (2006).
[Crossref]

2005 (3)

V. Alchanatis, L. Ridel, A. Hetzroni, and L. Yaroslavsky, “Weed detection in multi-spectral images of cotton fields,” Comput. Electron. Agric. 47(3), 243–260 (2005).
[Crossref]

J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
[Crossref]

N. Gupta, “Acousto-optic-tunable-filter-based spectropolarimetric imagers for medical diagnostic applications-instrument design point of view,” J. Biomed. Opt. 10(5), 051802 (2005).
[Crossref] [PubMed]

2004 (3)

T. Tanaka, H. Park, and S. Hattori, “Measurement of forest canopy structure by a laser plane range-finding method: improvement of radiative resolution and examples of its application,” Agric. For. Meteorol. 125(1-2), 129–142 (2004).
[Crossref]

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[Crossref]

D. R. Suhre, L. J. Denes, and N. Gupta, “Telecentric confocal optics for aberration correction of acousto-optic tunable filters,” Appl. Opt. 43(6), 1255–1260 (2004).
[Crossref] [PubMed]

2003 (1)

D. A. Glenar, D. L. Blaney, and J. J. Hillman, “AIMS: acousto-optic imaging spectrometer for spectral mapping of solid surfaces,” Acta Astronaut. 52(2-6), 389–396 (2003).
[Crossref]

2002 (1)

2001 (1)

1995 (1)

L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995).
[Crossref]

1994 (1)

1981 (1)

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

1976 (1)

I. C. Chang, “Acoustooptic devices and applications,” IEEE Trans. Sonics Ultrason. 23(1), 2–21 (1976).
[Crossref]

1974 (1)

I. C. Chang, “Non-collinear acousto-optic filter with large angular aperture,” Appl. Phys. Lett. 25(7), 370–372 (1974).
[Crossref]

Aasen, H.

H. Aasen, A. Burkart, A. Bolten, and G. Bareth, “Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance,” ISPRS J. Photogramm. Remote Sens. 108(5), 245–259 (2015).
[Crossref]

Adams, J.

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
[Crossref]

Aguiar, L.

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
[Crossref]

Alchanatis, V.

V. Alchanatis, L. Ridel, A. Hetzroni, and L. Yaroslavsky, “Weed detection in multi-spectral images of cotton fields,” Comput. Electron. Agric. 47(3), 243–260 (2005).
[Crossref]

Alves, F.

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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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Fischler, M. A.

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Gastellu-Etchegorry, J.

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L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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Morsdorf, F.

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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Mulqueen, M.

J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
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Muñz, J.

J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
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Ni-Meister, W.

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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Odhner, J. E.

L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995).
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Oerke, E.

J. Behmann, A. Mahlein, S. Paulus, J. Dupuius, H. kuhlmann, E. Oerke, and L. Plumer, “Generation and application of hyperspectral 3D plant models: methods and challenges,” Mach. Vis. Appl. 27(5), 611–624 (2016).
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Ozga, I.

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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Papadakis, V.

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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J. Behmann, A. Mahlein, S. Paulus, J. Dupuius, H. kuhlmann, E. Oerke, and L. Plumer, “Generation and application of hyperspectral 3D plant models: methods and challenges,” Mach. Vis. Appl. 27(5), 611–624 (2016).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
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J. E. Anderson, L. C. Plourde, M. E. Martin, B. H. Braswell, M. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
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Plumer, L.

J. Behmann, A. Mahlein, S. Paulus, J. Dupuius, H. kuhlmann, E. Oerke, and L. Plumer, “Generation and application of hyperspectral 3D plant models: methods and challenges,” Mach. Vis. Appl. 27(5), 611–624 (2016).
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Poletto, L.

N. Brusco, S. Capeleto, M. Fedel, A. Paviotti, L. Poletto, G. M. Cortelazzo, and G. Tondello, “A system for 3D modeling frescoed historical buildings with multispectral texture information,” Mach. Vis. Appl. 17(6), 373–393 (2006).
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Portalés, C.

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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Powers, M. A.

Prum, R. O.

M. H. Kim, T. A. Harvey, D. S. Kittle, H. Rushmeier, J. Dorsey, R. O. Prum, and B. J. Javidi, “3D imaging spectroscopy for measuring hyperspectral patterns on solid objects,” ACM Trans. Graph. 31(4), 13–15 (2012).
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S. L. Liang, X. Zhao, S. H. Liu, W. P. Yuan, X. Cheng, Z. Q. Xiao, X. T. Zhang, Q. Liu, J. Cheng, H. R. Tang, T. H. Qu, Y. C. Bo, Y. Qu, H. Z. Ren, K. Yu, and J. Townshend, “A long-term global land surface satellite (GLASS) data-set for environmental studies,” Int. J. Digit. Earth 6(sup1), 5–33 (2013).
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S. L. Liang, X. Zhao, S. H. Liu, W. P. Yuan, X. Cheng, Z. Q. Xiao, X. T. Zhang, Q. Liu, J. Cheng, H. R. Tang, T. H. Qu, Y. C. Bo, Y. Qu, H. Z. Ren, K. Yu, and J. Townshend, “A long-term global land surface satellite (GLASS) data-set for environmental studies,” Int. J. Digit. Earth 6(sup1), 5–33 (2013).
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Rautiainen, M.

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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S. L. Liang, X. Zhao, S. H. Liu, W. P. Yuan, X. Cheng, Z. Q. Xiao, X. T. Zhang, Q. Liu, J. Cheng, H. R. Tang, T. H. Qu, Y. C. Bo, Y. Qu, H. Z. Ren, K. Yu, and J. Townshend, “A long-term global land surface satellite (GLASS) data-set for environmental studies,” Int. J. Digit. Earth 6(sup1), 5–33 (2013).
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L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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Rueda, H.

Rushmeier, H.

M. H. Kim, T. A. Harvey, D. S. Kittle, H. Rushmeier, J. Dorsey, R. O. Prum, and B. J. Javidi, “3D imaging spectroscopy for measuring hyperspectral patterns on solid objects,” ACM Trans. Graph. 31(4), 13–15 (2012).
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Saif, B.

Sánchez, A. J.

E. Ivorra, S. Verdu, A. J. Sánchez, R. Grau, and J. M. Barat, “Predicting gilthead sea bream (sparus aurata) freshness by a novel combined technique of 3D imaging and SW-NIR spectral analysis,” Sensors (Basel) 16(10), 1735 (2016).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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F. Zhao, Y. G. Li, X. Dai, W. Verhoef, Y. Q. Guo, H. Shang, X. F. Gu, Y. B. Huang, T. Yu, and J. X. Huang, “Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops,” Remote Sens. Environ. 156, 129–142 (2015).
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Shi, S. G.

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Shure, D. R.

L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995).
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Sirmacek, B.

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
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Smith, M.

J. E. Anderson, L. C. Plourde, M. E. Martin, B. H. Braswell, M. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
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J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
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Tanaka, T.

T. Tanaka, H. Park, and S. Hattori, “Measurement of forest canopy structure by a laser plane range-finding method: improvement of radiative resolution and examples of its application,” Agric. For. Meteorol. 125(1-2), 129–142 (2004).
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Proc. SPIE (2)

L. H. Taylor, D. R. Shure, S. A. Wutzke, P. L. Ulerich, G. D. Baldwin, M. T. Meyers, and J. E. Odhner, “Infrared spectroradiometer design based on an acousto-optic tunable filter,” Proc. SPIE 2480, 334–345 (1995).
[Crossref]

L. Granero-Montagud, C. Portalés, B. Pastor-Carbonell, E. Ribes-Gómez, A. Gutiérrez-Lucas, V. Tornari, V. Papadakis, R. M. Groves, B. Sirmacek, A. Bonazza, I. Ozga, J. Vermeiren, K. Zanden, M. Föster, P. Aswendt, A. Borreman, J. D. Ward, A. Cardoso, L. Aguiar, F. Alves, P. Ropret, J. M. Luzón-Nogué, and C. Dietz, “SYDDARTA: new methodology for digitization of deterioration estimation in paintings,” Proc. SPIE 8790, 879011 (2013).
[Crossref]

Real-Time Imaging (1)

J. Vila, J. Calpea, F. Pla, L. Gómez, J. Connell, J. Marchant, J. Calleja, M. Mulqueen, J. Muñz, and A. Klaren, “SmartSpectra: applying multispectral imaging to industrial environments,” Real-Time Imaging 11(2), 85–98 (2005).
[Crossref]

Remote Sens. (3)

H. J. Zhao, S. G. Shi, X. F. Gu, G. R. Jia, and L. B. Xu, “Integrated system for auto-registered hyperspectral and 3D structure measurement at the point scale,” Remote Sens. 9(6), 512 (2017).
[Crossref]

J. Geipel, J. Link, and W. Claupein, “Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system,” Remote Sens. 6(11), 10335–10355 (2014).
[Crossref]

J. Gastellu-Etchegorry, T. G. Yin, T. Cajgfinger, and E. Grau, “Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes,” Remote Sens. 7(2), 1667–1701 (2015).
[Crossref]

Remote Sens. Environ. (3)

J. E. Anderson, L. C. Plourde, M. E. Martin, B. H. Braswell, M. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair, “Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest,” Remote Sens. Environ. 112(4), 1856–1870 (2008).
[Crossref]

F. Zhao, Y. G. Li, X. Dai, W. Verhoef, Y. Q. Guo, H. Shang, X. F. Gu, Y. B. Huang, T. Yu, and J. X. Huang, “Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops,” Remote Sens. Environ. 156, 129–142 (2015).
[Crossref]

J. L. Widlowski, C. Mio, M. Disney, J. Adams, I. Andredakis, C. Atzberger, J. Brennan, L. Busetto, M. Chelle, G. Ceccherini, R. Colombo, J. F. Cote, A. Eenmae, R. Essery, J. P. Gastellu-Etchegorry, N. Gobron, E. Grau, V. Haverd, L. Homolova, H. G. Huang, L. Hunt, H. Kobayashi, B. Koetz, A. Kuusk, J. Kuusk, M. Lang, P. E. Lewis, J. L. Lovell, Z. Malenovsky, M. Meroni, F. Morsdorf, M. Mottus, W. Ni-Meister, B. Pinty, M. Rautiainen, M. Schlerf, B. Somers, J. Stuckens, M. M. Verstraete, W. Z. Yang, F. Zhao, and T. Zenone, “The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing,” Remote Sens. Environ. 169, 418–437 (2015).
[Crossref]

Res. Astron. Astrophys. (1)

Z. P. He, B. Y. Wang, G. Lü, C. L. Li, L. Y. Yuan, R. Xu, B. Liu, K. Chen, and J. Y. Wang, “Operating principles and detection characteristics of the visible and near-infrared imaging spectrometer in the Chang’e-3,” Res. Astron. Astrophys. 14(12), 1567–1577 (2014).
[Crossref]

Sensors (Basel) (1)

E. Ivorra, S. Verdu, A. J. Sánchez, R. Grau, and J. M. Barat, “Predicting gilthead sea bream (sparus aurata) freshness by a novel combined technique of 3D imaging and SW-NIR spectral analysis,” Sensors (Basel) 16(10), 1735 (2016).
[Crossref] [PubMed]

Other (2)

J. Liang, A. Zia, J. Zhou, and X. Sirault, “3D plant modelling via hyperspectral imaging,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2013), pp. 172–177.

P. W. Zhou, H. J. Zhao, Y. Zhang, and C. C. Li, “Accurate optical design of an acousto-optic tunable filter imaging spectrometer,” in Proceedings of IEEE International Conference on Imaging System and Techniques (IEEE, 2012), pp. 249–253.
[Crossref]

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Figures (16)

Fig. 1
Fig. 1 Schematic diagram of non-collinear AOTF.
Fig. 2
Fig. 2 Wave vector diagram for the non-collinear AOTF.
Fig. 3
Fig. 3 Schematic diagram of light propagation for the non-collinear AOTF.
Fig. 4
Fig. 4 Simplified schematic diagram of laser triangulation system.
Fig. 5
Fig. 5 Schematic diagram of the 3D hyperspectral imaging system based on AOTF.
Fig. 6
Fig. 6 Optical structures of AOTF system: (a) Collimated optics; (b) Confocal optics. In this type of spectrometers, the Etendue is determined by the linear and angular apertures of AOTF. Commonly, in collimated optics, the aperture stop is usually on the surface of crystal and equal to linear aperture, and the angle of incidence should be equal to angular aperture to make maximum Etendue. In confocal optics, on the other hand, the image plane is located at AOTF exit surface and equal to linear aperture, the aperture stop is positioned at focal point of fore optics to ensure the incident angle equal to angular aperture by telecentric structure.
Fig. 7
Fig. 7 Partial enlarged drawing of grid distortion: (a) Undiffracted channel in confocal optics; (b) Diffracted channel in confocal optics; (c) Undiffracted channel in collimated optics; (d) Diffracted channel in collimated optics.
Fig. 8
Fig. 8 Imaging system design: (a) Prototype; (b) Layout. The instrument has compact structure and is convenient for field experiments. The external dimension is 335mm × 260mm × 130mm.
Fig. 9
Fig. 9 Spectral detection test. (a) The test scene; (b) The spectral image at 715nm obtained by prototype.
Fig. 10
Fig. 10 Spectral reflectivity of leaves on region of interest obtained by ASD and prototype. (a) The spectral curves; (b) Absolute error and relative error.
Fig. 11
Fig. 11 Plane fitting test: (a) The test scene; (b) Residuals of the plane fitting.
Fig. 12
Fig. 12 3D measurement test: (a) The standard stepped sample; (b) Point cloud; (c) Method.
Fig. 13
Fig. 13 Images of checkerboard target: (a) Undiffracted channel; (b) Diffracted channel.
Fig. 14
Fig. 14 Registration accuracy measurement: (a) Feature point extraction by SIFT; (b) Removal of mismatching points by RANSAC.
Fig. 15
Fig. 15 A potted plant is detected by 3D hyperspectral imaging system at noon. (a) The outdoor experiment scene; (b) The spectral image in the band of 563 nm; (c) The panchromatic image of target with laser scanning points.
Fig. 16
Fig. 16 The unified information includes panchromatic image, 3D structure and spectral reflectance.

Tables (1)

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Table 1 Characteristics of the imaging system

Equations (12)

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k i ± k a = k d ,
k i = 2 π n i λ 0 , k d = 2 π n d λ 0 , k a = 2 π Λ ,
n i = n o ,
n d = [ cos 2 ( θ d ) / n o 2 + sin 2 ( θ d ) / n e 2 ] 1 / 2 .
z c [ u v 1 ] = [ f x 0 u 0 0 f y v 0 0 0 1 ] [ x c y x z c ] A [ x c y x z c ] ,
x c f y ( u u 0 ) = y c f x ( v v 0 ) = z c f x f y ,
x c x 2 a 2 = y c y 2 b 2 = z c z 2 c 2 ,
x c f y ( u u 0 ) = y c f x ( v v 0 ) = z c f x f y = s , x c x 2 a 2 = y c y 2 b 2 = z c z 2 c 2 = t .
{ p 1 = ( s f y ( u u 0 ) , s f x ( v v 0 ) , s f x f y ) p 2 = ( x 2 + t a 2 , y 2 + t b 2 , z 2 + t c 2 ) .
x c = s f y ( u u 0 ) + x 2 + t a 2 2 , y c = s f x ( v v 0 ) + y 2 + t b 2 2 , z c = s f x f y + z 2 + t c 2 2 .
A e r r o r = R P r o t o t y e ( λ ) R A S D ( λ ) , R e r r o r = R P r o t o t y e ( λ ) R A S D ( λ ) R A S D ( λ ) × 100 % .
e r r o r R M S = i = 1 k [ ( x u x d ) 2 + ( y u y d ) 2 ] k = 0.63 pixels .

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