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Article

Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

1
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2
Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Hangzhou 310020, China
3
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Academic Editor: Akira Iwasaki
Remote Sens. 2021, 13(16), 3147; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163147
Received: 21 June 2021 / Revised: 26 July 2021 / Accepted: 5 August 2021 / Published: 9 August 2021
Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing. View Full-Text
Keywords: hyperspectral unmixing; spectral–spatial model; autoencoder network; gating mechanism hyperspectral unmixing; spectral–spatial model; autoencoder network; gating mechanism
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MDPI and ACS Style

Hua, Z.; Li, X.; Jiang, J.; Zhao, L. Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing. Remote Sens. 2021, 13, 3147. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163147

AMA Style

Hua Z, Li X, Jiang J, Zhao L. Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing. Remote Sensing. 2021; 13(16):3147. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163147

Chicago/Turabian Style

Hua, Ziqiang, Xiaorun Li, Jianfeng Jiang, and Liaoying Zhao. 2021. "Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing" Remote Sensing 13, no. 16: 3147. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163147

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