SSANet: An Adaptive Spectral–Spatial Attention Autoencoder Network for Hyperspectral Unmixing

Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their performance by the fact that they treat all spectr...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 15; číslo 8; s. 2070
Hlavní autoři: Wang, Jie, Xu, Jindong, Chong, Qianpeng, Liu, Zhaowei, Yan, Weiqing, Xing, Haihua, Xing, Qianguo, Ni, Mengying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2023
Témata:
Artificial intelligence
autoencoder
Band spectra
Comparative analysis
data collection
deep learning
Design
geometry
hyperspectral imagery
Hyperspectral imaging
hyperspectral unmixing
Image processing
Machine learning
Methods
Modules
Multispectral photography
Neural networks
Pixels
Regularization
Remote sensing
Sparsity
Spatial data
Spectral bands
spectral–spatial attention mechanism
Synthetic data
Technology application
China
ISSN:2072-4292, 2072-4292
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Shrnutí:Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their performance by the fact that they treat all spectral bands and spatial information equally in the unmixing procedure. In this article, we propose an adaptive spectral–spatial attention autoencoder network, called SSANet, to solve the mixing pixel problem of the hyperspectral image. First, we design an adaptive spectral–spatial attention module, which refines spectral–spatial features by sequentially superimposing the spectral attention module and spatial attention module. The spectral attention module is built to select useful spectral bands, and the spatial attention module is designed to filter spatial information. Second, SSANet exploits the geometric properties of endmembers in the hyperspectral image while considering abundance sparsity. We significantly improve the endmember and abundance results by introducing minimum volume and sparsity regularization terms into the loss function. We evaluate the proposed SSANet on one synthetic dataset and four real hyperspectral scenes, i.e., Samson, Jasper Ridge, Houston, and Urban. The results indicate that the proposed SSANet achieved competitive unmixing results compared with several conventional and advanced unmixing approaches with respect to the root mean square error and spectral angle distance.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15082070