Abstract
The computed tomography imaging spectrometer (CTIS) is a snapshot hyper-spectral imaging system which has recently been demonstrated of value when used in a compressed learning mode. In such a mode, the raw data are not reconstructed in an hyperspectral cube but are directly transmitted to a neural network to perform classification. While the previous investigations on this topic were limited to a simulation perspective, we extend these results to real images and demonstrate the possibility to train the network on simulated data and apply this trained model successfully on real images.
© 2021 The Author(s)
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