A Review of Hyperspectral Unmixing Considering Spectral Variability

Abstract

Hyperspectral unmixing (HU) is a fundamental task in the analysis and interpretation of hyperspectral images. A large number of HU methods have been developed rapidly, and several review papers have summarized these developments. More recently, spectral variability (SV) has attracted increasing attention in HU, leading to the emergence of various SV-aware hyperspectral unmixing (SVHU) methods. While existing reviews cover general HU, dedicated surveys on SVHU remain scarce. Moreover, most relevant reviews focus on a specific methodological branch and fail to address the latest advances. To address this gap, we present a comprehensive review of SVHU methods. To the best of authors’ knowledge, this is the first work to propose a unified SVHU framework that covers both single-temporal and multitemporal settings. Existing SVHU methods are systematically categorized into three groups: model-driven, data-driven, and model–data-driven approaches, each of which is discussed in detail. Subsequently, we describe the datasets and evaluation metrics, and compare several representative state-of-the-art methods on multiple commonly used public datasets. In addition, we summarize related downstream applications, including hyperspectral super-resolution, classification, and change detection. Finally, we discuss the main challenges and outline several promising directions for future research.