Spectral Image Classification From Multi-Sensor Compressive Measurements

Abstract

Spectral image classification is an active research topic in remote sensing. In this sense, various multi-sensor spectral image fusion algorithms have been recently evaluated via pixel-based classification. In general, the sizes of multi-sensor images challenge the storing and processing capabilities of sensing systems. Therefore, different image fusion algorithms from measurements captured by multi-resolution compressive spectral imaging (CSI) sensors have been proposed. However, the computational costs for reconstructing and fusing spectral images from compressive measurements are high, and these approaches do not consider the huge amount of information embedded in acquired data. In this article, a spectral image classification scheme from multi-sensor CSI projections is developed. Specifically, this scheme includes a feature extraction procedure that exploits the fact that CSI data contain relevant information of the spectral image, and therefore, low-dimensional features can be obtained from measurements. Furthermore, a fusion model is presented to combine the information of the extracted features with the aim of estimating high-resolution classification attributes. Then, a pixel-based classifier is applied to the fused features with the goal of labeling the corresponding high-resolution spectral image. The performance of the proposed classification scheme is compared to other methods on the Salinas Valley data set for different supervised classifiers and various downsampling settings. Extensive simulations on the Pavia University data set are also shown, where the proposed method outperforms other classification approaches that reconstruct and fuse from compressive measurements. Finally, the effectiveness of the proposed classification approach is validated in real multi-sensor data.