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.

在压缩光谱成像（CSI）框架中，已提出了不同的体系结构以从压缩测量中恢复高分辨率光谱图像。由于CSI架构紧凑地捕获了光谱图像的相关信息，因此最近提出了各种从压缩样本中提取分类特征的方法。然而，这些技术需要特征提取过程，该特征提取过程使用嵌入在编码孔径图案中的信息对测量进行重新排序。本文提出了一种将特征与双分辨率压缩测量结果直接融合的方法，用于光谱图像分类。更准确地说，融合方法被公式化为一个反问题，该反问题可以从压缩测量值中估算出高空间分辨率和低维特征带。为此，使用压缩孔径模式中嵌入的信息，对将压缩测量描述为融合特征的降级版本的抽取矩阵进行数学建模。此外，为了考虑到相邻像素之间的相关性，我们在融合问题中同时包括了稀疏度提升和总变化（TV）正则化项，从而提高了基于像素的分类器的准确性。为了解决融合问题，我们描述了一种基于乘法器交替方向方法（accelerated-ADMM）的加速变体的算法。此外，介绍了一种分类方法，其中包括开发的融合方法和多层神经网络。最后，在三个遥感光谱图像和一组在实验室中捕获的压缩测量值上评估了所提出的方法。大量的仿真表明，在各种性能指标下，建议的分类方法优于其他方法。