Learning-based classification approach for coded aperture compressive spectral image

Abstract

Spectral images include rich spatio-spectral information of target scene, which can accurately identify and distinguish the features of ground objects. Therefore, spectral image classification is widely used in remote sensing. However, traditional spectral imaging techniques need to scan the region of interest along the spatial dimension or spectral dimension, which takes a long acquisition time and increases the burden of data transmission and storage. To overcome these shortcomings, coded aperture snapshot spectral imaging (CASSI) system based on compressive sensing theory appeared. In this paper, we build a testbed of dual-disperser CASSI (DD-CASSI) system, which can reconstruct the three-dimensional (3D) spectral image datacube of target object from a few two-dimensional compressive measurements. Then, a 3D convolutional neural network is applied to accomplish the spectral image classification based on the reconstructed datacube. Different classification methods are compared based on the experimental data. It shows that the proposed compressive spectral image classification method achieves pretty close results compared to the classification methods based on the original datacube. But, the proposed method is beneficial to improve the acquisition efficiency of the spectral image data.