期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2022.3152782
关键词
Hyperspectral imaging; Smoothing methods; Feature extraction; Deep learning; Convolution; Data mining; Task analysis; Autoencoder (AE); deep learning; global and local; hyperspectral unmixing
类别
资金
- National Key Research and Development Program of China [2019YFC1510905]
- National Natural Science Foundation of China [62001251, 62001252]
- China Postdoctoral Science Foundation [2020M670631]
This article introduces a deep network for hyperspectral unmixing based on a new global-local smoothing autoencoder (GLA) that aims to explore the local homogeneity and global self-similarity of hyperspectral imagery. The proposed GLA network includes two modules: a Local Continuous conditional random field Smoothing (LCS) module and a global recurrent smoothing (GRS) module, which show promising results in abundance estimation compared to state-of-the-art unmixing methods.
Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures (endmembers) and their proportions (abundances). Recently, deep learning-based methods have been applied to enhance the representation ability of unmixing models by extracting joint spatial-spectral characteristics of the hyperspectral data. However, most deep learning based-unmixing methods usually conduct global smoothing by convolutions on the whole hyperspectral imagery, which may ignore the variations within the imagery and result in oversmoothing. In this article, we propose a deep network for hyperspectral unmixing based on a new global-local smoothing autoencoder (GLA). GLA is an unsupervised model, which aims at exploring the local homogeneity and the global self-similarity of hyperspectral imagery. The proposed GLA network mainly includes two modules: a Local Continuous conditional random field Smoothing (LCS) module and a global recurrent smoothing (GRS) module. In LCS, we propose a conditional random field-based smoothing strategy to describe the joint spatial-spectral information within a local homogeneity region, which also reduces the risk of abundance maps boundary blurry. In GRS, we follow the self-similarity assumption for hyperspectral imagery and develop a recurrent neural network structure to exploit potential long-distance dependency relationships among pixels. The GLA is compared with several state-of-the-art unmixing methods on both real and synthetic data, and the abundance estimation results indicate that our method is promising. We will publish the code of GLA if this article has the honor to be accepted.
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