Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review

Abstract

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of Earth’s surface at a distance of data acquisition devices. The recent advancement and even revolution of HS RS techniques offer opportunities to realize the potential of various applications while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3D HS inherent structure, tensor decomposition has aroused widespread concern and spurred research in HS data processing tasks over the past decades. In this article, we aim to present a comprehensive overview of tensor decomposition, specifically contextualizing the five broad topics in HS data processing: HS restoration, compressive sensing (CS), anomaly detection (AD), HS–multispectral (MS) fusion, and spectral unmixing (SU). For each topic, we elaborate on the remarkable achievements of tensor decomposition models for HS RS, with a pivotal description of the existing methodologies and a representative exhibition of experimental results. As a result, the remaining challenges of the follow-up research directions are outlined and discussed from the perspective of actual HS RS practices and tensor decomposition merged with advanced priors and even deep neural networks. This article summarizes different tensor decomposition-based HS data processing methods and categorizes them into different classes, from simple adoptions to complex combinations with other priors for algorithm beginners. We expect that this survey provides new investigations and development trends for experienced researchers to some extent.