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.
Snapshot Compressive Spectral Imaging (SCPI) is a computational imaging technique that reconstructs three-dimensional (3D) spectral datacube from two-dimensional (2D) compressive measurements. The dual-disperser coded aperture snapshot spectral imaging (DD-CASSI) system is one of the prototypes to implement the SCPI technique. It can simultaneously acquire and compress the spectral images of target scene, and then the spectral images can be reconstructed from the compressive measurements. Some image priors such as Deep Image Prior (DIP), sparsity prior, low-rank prior and Total Variation (TV) prior can be used to improve the performance of different SCPI reconstruction algorithms. In this paper, we compare the spectral image reconstruction approaches based on the split Bregman algorithm combined with different image priors. These algorithms are assessed based on both simulation data and experimental testbed of DD-CASSI system. Simulation and experimental results show that the DIP prior can achieve better reconstruction performance compared to the other three image priors.
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