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Compressive spectral imaging (CSI) has emerged as an attractive compressionand sensing technique, primarily to sense spectral regions where traditionalsystems result in highly costly such as in the near-infrared spectrum.Recently, it has been shown that spectral classification can be performeddirectly in the compressive domain, considering the amount of spectralinformation embedded in the measurements, skipping the reconstruction step.Consequently, the classification quality directly depends on the set of codingpatterns employed in the sensing step. Therefore, this work proposes anend-to-end approach to jointly design the coding patterns used in CSI and thenetwork parameters to perform spectral classification directly from theembedded near-infrared compressive measurements. Extensive simulation on thethree-dimensional coded aperture snapshot spectral imaging (3D-CASSI) systemvalidates that the proposed design outperforms traditional and random design inup to 10% of classification accuracy.

Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification