  Hyperspectral image classification (HIC) is an active research topic in
remote sensing. Hyperspectral images typically generate large data cubes posing
big challenges in data acquisition, storage, transmission and processing. To
overcome these limitations, this paper develops a novel deep learning HIC
approach based on compressive measurements of coded-aperture snapshot spectral
imagers (CASSI), without reconstructing the complete hyperspectral data cube. A
new kind of deep learning strategy, namely 3D coded convolutional neural
network (3D-CCNN) is proposed to efficiently solve for the classification
problem, where the hardware-based coded aperture is regarded as a pixel-wise
connected network layer. An end-to-end training method is developed to jointly
optimize the network parameters and the coded apertures with periodic
structures. The accuracy of classification is effectively improved by
exploiting the synergy between the deep learning network and coded apertures.
The superiority of the proposed method is assessed over the state-of-the-art
HIC methods on several hyperspectral datasets.
