  In the compressive spectral imaging (CSI) framework, different architectures
have been proposed to recover high-resolution spectral images from compressive
measurements. Since CSI architectures compactly capture the relevant
information of the spectral image, various methods that extract classification
features from compressive samples have been recently proposed. However, these
techniques require a feature extraction procedure that reorders measurements
using the information embedded in the coded aperture patterns. In this paper, a
method that fuses features directly from dual-resolution compressive
measurements is proposed for spectral image classification. More precisely, the
fusion method is formulated as an inverse problem that estimates
high-spatial-resolution and low-dimensional feature bands from compressive
measurements. To this end, the decimation matrices that describe the
compressive measurements as degraded versions of the fused features are
mathematically modeled using the information embedded in the coded aperture
patterns. Furthermore, we include both a sparsity-promoting and a
total-variation (TV) regularization terms to the fusion problem in order to
consider the correlations between neighbor pixels, and therefore, improve the
accuracy of pixel-based classifiers. To solve the fusion problem, we describe
an algorithm based on the accelerated variant of the alternating direction
method of multipliers (accelerated-ADMM). Additionally, a classification
approach that includes the developed fusion method and a multilayer neural
network is introduced. Finally, the proposed approach is evaluated on three
remote sensing spectral images and a set of compressive measurements captured
in the laboratory. Extensive simulations show that the proposed classification
approach outperforms other approaches under various performance metrics.
