Spectral Variability-Aware Cascaded Autoencoder for Hyperspectral Unmixing

Abstract

Spectral variability inevitably presents in hyperspectral images (HSIs), resulting in significant unmixing errors when using the conventional linear mixture model (LMM). Though several variants of LMM have been proposed to encounter such spectral variability, they cannot well model the complex characteristics of spectral variability, and the performance of these variants strongly depends on the prior knowledge of the scene. In this article, spectral variability within an image is classified into class-dependent variability and class-independent one, which can be tackled by a novel fully linear mixture model (FLMM) introducing a class-dependent multiplicative scaling term, a class-dependent additive perturbation term, and a class-independent variability term into the conventional LMM. Moreover, a spectral variability-aware cascaded autoencoder (SVACA) is designed to realize the automatic learning and representation of unmixing targets and spectral variability in different hyperspectral scenarios, which consists of a class-independent variability autoencoder and a cascaded class-dependent variability autoencoder. Such a network is able to handle different spectral variability autonomously without any scene prior by parallel inference structure. Experimental results over synthetic and real hyperspectral datasets demonstrate that the proposed SVACA network not only outperforms several state-of-the-art unmixing networks but also presents a stronger capability to handle spectral variability within HSIs.