Abstract

This paper describes an approach of machine-learning pattern recognition procedures for the land surface objects using their spectral and textural features on remotely sensed hyperspectral images together with the biological parameters retrieval for the recognized classes of forests. Modified Bayesian classifier is used to improve the related procedures in spatial and spectral domains. Direct and inverse problems of atmospheric optics are solved based on modeling results of the projective cover and density of the forest canopy for the selected classes of forests of different species and ages. Applying the proposed techniques to process images of high spectral and spatial resolution, we have detected object classes including forests within their contours on a particular image and can retrieve the phytomass amount of leaves/needles as well as the relevant total biomass amount for the forest canopy.

© 2015 Optical Society of America

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References

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  1. C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
    [Crossref]
  2. V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).
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    [Crossref] [PubMed]
  4. G. Vane and A. F. H. Goetz, “Terrestrial imaging spectroscopy,” Remote Sens. Environ. 24(1), 1–29 (1988).
    [Crossref]
  5. P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
    [Crossref]
  6. V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
    [Crossref] [PubMed]
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  13. M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
    [Crossref]
  14. V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
    [Crossref]
  15. Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
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  19. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
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  23. S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1137–1146 (1991).
  24. V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).
    [Crossref]
  25. G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
    [Crossref]
  26. N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sens. Environ. 76(2), 156–172 (2001).
    [Crossref]
  27. V. V. Kozoderov, “A scientific approach to employ monitoring and modeling techniques for Global Change and Terrestrial Ecosystems and other related projects,” J. Biogeogr. 22(4/5), 927–933 (1995).
    [Crossref]
  28. P. North, “Estimation of fAPAR, LAI and vegetation fractional cover from ATSR-2 imagery,” Remote Sens. Environ. 80(1), 114–121 (2002).
    [Crossref]
  29. J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
    [Crossref]
  30. G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
    [Crossref]

2015 (1)

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

2014 (5)

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data,” Izv., Atmos. Ocean. Phys. 50(9), 943–952 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

P. Artal, “Optics of the eye and its impact in vision,” Adv. Opt. Photonics 6(3), 340–367 (2014).
[Crossref]

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

2012 (2)

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

2011 (1)

V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).
[Crossref]

2009 (1)

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

2008 (1)

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

2007 (1)

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

2004 (1)

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

2002 (1)

P. North, “Estimation of fAPAR, LAI and vegetation fractional cover from ATSR-2 imagery,” Remote Sens. Environ. 80(1), 114–121 (2002).
[Crossref]

2001 (1)

N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sens. Environ. 76(2), 156–172 (2001).
[Crossref]

2000 (2)

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000).
[Crossref]

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

1995 (1)

V. V. Kozoderov, “A scientific approach to employ monitoring and modeling techniques for Global Change and Terrestrial Ecosystems and other related projects,” J. Biogeogr. 22(4/5), 927–933 (1995).
[Crossref]

1993 (1)

L. Herault and R. Horaud, “Figure-ground discrimination: A combinatorial optimization approach,” IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 899–914 (1993).
[Crossref]

1992 (1)

N. S. Friedland and A. Rosenfeld, “Compact object recognition using energy-function based optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 770–777 (1992).
[Crossref]

1991 (2)

Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
[Crossref]

S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1137–1146 (1991).

1988 (2)

G. Vane and A. F. H. Goetz, “Terrestrial imaging spectroscopy,” Remote Sens. Environ. 24(1), 1–29 (1988).
[Crossref]

M. Bertero, T. A. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76(8), 869–889 (1988).
[Crossref]

1985 (1)

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

Artal, P.

P. Artal, “Optics of the eye and its impact in vision,” Adv. Opt. Photonics 6(3), 340–367 (2014).
[Crossref]

Ashton, M. S.

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

Baret, F.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

Belward, A.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

Bertero, M.

M. Bertero, T. A. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76(8), 869–889 (1988).
[Crossref]

Berveiller, D.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Breda, N.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Broge, N. H.

N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sens. Environ. 76(2), 156–172 (2001).
[Crossref]

Bruzzone, L.

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

Cernuschi-Frias, B.

Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
[Crossref]

Chen, Q.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Cheng, H.

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

Coomes, D. A.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Cooper, D. B.

Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
[Crossref]

Dalponte, M.

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

Davi, H.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Del Frate, F.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Dmitriev, E. V.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data,” Izv., Atmos. Ocean. Phys. 50(9), 943–952 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).
[Crossref]

Dufrene, E.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Duin, R. P. W.

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000).
[Crossref]

Enclona, E. A.

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

Francois, C.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Friedland, N. S.

N. S. Friedland and A. Rosenfeld, “Compact object recognition using energy-function based optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 770–777 (1992).
[Crossref]

Genet, H.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Gianelle, D.

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

Goetz, A. F. H.

G. Vane and A. F. H. Goetz, “Terrestrial imaging spectroscopy,” Remote Sens. Environ. 24(1), 1–29 (1988).
[Crossref]

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

Guerriero, L.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Han, J.

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

Herault, L.

L. Herault and R. Horaud, “Figure-ground discrimination: A combinatorial optimization approach,” IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 899–914 (1993).
[Crossref]

Horaud, R.

L. Herault and R. Horaud, “Figure-ground discrimination: A combinatorial optimization approach,” IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 899–914 (1993).
[Crossref]

Hung, Y. P.

Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
[Crossref]

Jain, A. K.

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000).
[Crossref]

Justice, C.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

Justice, C. O.

S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1137–1146 (1991).

Kamentsev, V. P.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data,” Izv., Atmos. Ocean. Phys. 50(9), 943–952 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

Kondranin, T. V.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

Kozoderov, V. V.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data,” Izv., Atmos. Ocean. Phys. 50(9), 943–952 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).
[Crossref]

V. V. Kozoderov, “A scientific approach to employ monitoring and modeling techniques for Global Change and Terrestrial Ecosystems and other related projects,” J. Biogeogr. 22(4/5), 927–933 (1995).
[Crossref]

Laurin, G. V.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

le Maire, G.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Leblanc, E.

N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sens. Environ. 76(2), 156–172 (2001).
[Crossref]

Lewis, P.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

Lindsell, J. A.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Mao, J.

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000).
[Crossref]

Morisette, J. T.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

North, P.

P. North, “Estimation of fAPAR, LAI and vegetation fractional cover from ATSR-2 imagery,” Remote Sens. Environ. 80(1), 114–121 (2002).
[Crossref]

Packalen, P.

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

Pirotti, F.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Poggio, T. A.

M. Bertero, T. A. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76(8), 869–889 (1988).
[Crossref]

Pontailler, J.-Y.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Prince, S. D.

S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1137–1146 (1991).

Privette, J. L.

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

Rock, B. N.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

Rosenfeld, A.

N. S. Friedland and A. Rosenfeld, “Compact object recognition using energy-function based optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 770–777 (1992).
[Crossref]

Seppanen, A.

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

Sokolov, A. A.

Solomon, J. E.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

Soudani, K.

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

Thenkabail, P. S.

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

Tokola, T.

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

Torre, V.

M. Bertero, T. A. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76(8), 869–889 (1988).
[Crossref]

Valentini, R.

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Van Der Meer, B.

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

Vane, G.

G. Vane and A. F. H. Goetz, “Terrestrial imaging spectroscopy,” Remote Sens. Environ. 24(1), 1–29 (1988).
[Crossref]

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

Vauhkonen, J.

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

Vescovo, L.

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

Xin, D.

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

Yan, X.

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

Adv. Opt. Photonics (1)

P. Artal, “Optics of the eye and its impact in vision,” Adv. Opt. Photonics 6(3), 340–367 (2014).
[Crossref]

Adv. Space Res. (1)

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000).
[Crossref]

L. Herault and R. Horaud, “Figure-ground discrimination: A combinatorial optimization approach,” IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 899–914 (1993).
[Crossref]

N. S. Friedland and A. Rosenfeld, “Compact object recognition using energy-function based optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 770–777 (1992).
[Crossref]

Int. J. Comput. Vis. (1)

Y. P. Hung, D. B. Cooper, and B. Cernuschi-Frias, “Asymptotic Bayesian surface estimation using an image sequence,” Int. J. Comput. Vis. 6(2), 105–132 (1991).
[Crossref]

Int. J. Remote Sens. (4)

C. Justice, A. Belward, J. T. Morisette, P. Lewis, J. L. Privette, and F. Baret, “Developments in the validation of satellite sensor products for the study of the land surface,” Int. J. Remote Sens. 21(17), 3383–3390 (2000).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1137–1146 (1991).

V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).
[Crossref]

ISPRS J. Photogramm. Remote Sens. (1)

G. V. Laurin, Q. Chen, J. A. Lindsell, D. A. Coomes, F. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Izv., Atmos. Ocean. Phys. (2)

V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data,” Izv., Atmos. Ocean. Phys. 50(9), 943–952 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Mapping forest and peat fires using hyperspectral airborne remote-sensing data,” Izv., Atmos. Ocean. Phys. 48(9), 941–948 (2012).
[Crossref]

J. Biogeogr. (1)

V. V. Kozoderov, “A scientific approach to employ monitoring and modeling techniques for Global Change and Terrestrial Ecosystems and other related projects,” J. Biogeogr. 22(4/5), 927–933 (1995).
[Crossref]

Opt. Express (1)

Proc. IEEE (1)

M. Bertero, T. A. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76(8), 869–889 (1988).
[Crossref]

Remote Sens. Environ. (7)

G. Vane and A. F. H. Goetz, “Terrestrial imaging spectroscopy,” Remote Sens. Environ. 24(1), 1–29 (1988).
[Crossref]

P. S. Thenkabail, E. A. Enclona, M. S. Ashton, and B. Van Der Meer, “Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications,” Remote Sens. Environ. 91(3–4), 354–376 (2004).
[Crossref]

M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, “The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas,” Remote Sens. Environ. 113(11), 2345–2355 (2009).
[Crossref]

P. North, “Estimation of fAPAR, LAI and vegetation fractional cover from ATSR-2 imagery,” Remote Sens. Environ. 80(1), 114–121 (2002).
[Crossref]

J. Vauhkonen, A. Seppanen, P. Packalen, and T. Tokola, “Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data,” Remote Sens. Environ. 124, 534–541 (2012).
[Crossref]

G. le Maire, C. Francois, K. Soudani, D. Berveiller, J.-Y. Pontailler, N. Breda, H. Genet, H. Davi, and E. Dufrene, “Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass,” Remote Sens. Environ. 112(10), 3846–3864 (2008).
[Crossref]

N. H. Broge and E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density,” Remote Sens. Environ. 76(2), 156–172 (2001).
[Crossref]

Science (1)

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref] [PubMed]

WIREs Data Min. Knowl. Discov. (1)

J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” WIREs Data Min. Knowl. Discov. 15(1), 55–86 (2007).
[Crossref]

Other (5)

P. J. Curran, G. M. Foody, K. Ya. Kondratyev, V. V. Kozoderov, and P. P. Fedchenko, Remote Sensing of Soils and Vegetation in the USSR (Taylor and Francis, 1990).

K. Ya. Kondratyev, V. V. Kozoderov, and O. I. Smokty, Remote Sensing of the Earth from Space: Atmospheric Correction (Springer-Verlag, 1992).

R. O. Duda, P. E. Hart, and D. H. Stork, Pattern Classification (2nd ed.) (Wiley-Interscience, 2000).

S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, 1995).

V. V. Kozoderov, “Assessment of effect of the atmosphere as a clutter in recognition of natural formations from space,” in Airspace Studies of the Earth. Remote Sensing Data Processing Using Computer Means (Nauka, 1978), pp. 24–35.

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Figures (7)

Fig. 1
Fig. 1 Airborne hyperspectral imaging spectrometer (SPE “Lepton”, Russia). a) – external appearance, b) – the spectrometer installed on the gyro-stabilized platform during the airborne survey, c) –schematic representation.
Fig. 2
Fig. 2 Recognition of the forest tree species for the selected region with 7 plots: a) – the forest inventory map: orange – pine dominance, blue – birch dominance, the yellow frame indicates the location of the test area; b) – the photo image of the test area; c) – results of the recognition of the forest species composition taking into account the levels of the Sun illumination of the forest canopy (1 – shadowed, 2 – semi-sunlit, 3 – sunlit), magenta – unrecognized objects.
Fig. 3
Fig. 3 Block diagram of the inverse problem module.
Fig. 4
Fig. 4 Scheme of searching a partial solution of the inverse problem.
Fig. 5
Fig. 5 A test area of thematic interpretation of airborne hyperspectral data processing: RGB-synthesized image – a), the forest inventory map of the test area – b), the results of the recognition (dark-green –birch species, cyan – pine species, light-green – grass, black – roads, other objects) – c) and the forest species composition divided on several classes under coarse resolution (from pure conifer species – red color to pure deciduous species – blue color with the intermediate colors relating to the mixed forests) – d).
Fig. 6
Fig. 6 The results of the inverse problem solution represented as the projected cover area – a), cover density – b), crowns density – c).
Fig. 7
Fig. 7 Retrieval results of biological productivity of the forest stands: the volume of dry leaves phytomass – a), NPP values – b), NDVI values – c).

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