Multi-stage convolutional autoencoder network for hyperspectral unmixing

Abstract

Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and being amenable to mathematical treatment. Among the various linear unmixing methods, the autoencoder unmixing network has achieved superior performance and presented more significant potential because of the powerful data fitting ability and deep feature acquisition. However, the autoencoder unmixing network, focusing on the pixel-level associations, ignores the overall distribution and long-range dependencies of materials. Inspired by the receptive field mechanism and the effectiveness of multi-stage framework, we propose a multi-stage convolutional autoencoder network for hyperspectral linear unmixing, called MSNet. MSNet is capable of learning broad contextual information without losing the detailed features by the progressively multi-stage unmixing network in the unmixing process. Compared with the conventional single-stage unmixing methods, the multi-stage framework is more robust in solving the ill-posed unmixing problem. The proposed MSNet performs more effectively and competitively than state-of-the-art algorithms by comparison experiments on synthetic and real hyperspectral datasets. The source code is available at https://github.com/yuyang95/JAG-MSNet . • A multi-stage convolutional autoencoder network is proposed for hyperspectral unmixing. • The method is more robust in solving the ill-posed unmixing problem. • The method can incorporate global high-level contextual information without losing local details.