Multiresolution Compressive Feature Fusion for Spectral Image Classification

Compressive spectral imaging (CSI) has emerged as an alternative acquisition framework that simultaneously senses and compresses spectral images. In this context, the spectral image classification from CSI compressive measurements has become a challenging task since the feature extraction stage usually requires reconstructing the spectral image. Moreover, most approaches do not consider multi-sensor compressive measurements. In this paper, an approach for fusing features obtained from multi-sensor compressive measurements is proposed for spectral image classification. To this end, linear models describing low-resolution features as degraded versions of the high-resolution features are developed. Furthermore, an inverse problem is formulated aiming at estimating high-resolution features including both a sparsity-inducing term and a total variation (TV) regularization term to exploit the correlation between neighboring pixels of the spectral image, and therefore, to improve the performance of pixel-based classifiers. An algorithm based on the alternating direction method of multipliers (ADMM) is described for solving the fusion problem. The proposed feature fusion approach is tested for two CSI architectures: three-dimensional coded aperture snapshot spectral imaging (3D-CASSI) and colored CASSI (C-CASSI). Extensive simulations on various spectral image data sets show that the proposed approach outperforms other classification approaches under different performance criteria.