Dual Branch Autoencoder Network for Spectral-Spatial Hyperspectral Unmixing

Spatial information can play a supporting role in spectral unmixing. In this letter, we propose a dual branch autoencoder network to incorporate spatial-contextual information for spectral-spatial unmixing. The two branches leverage different architectures to efficiently extract spatial information...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autoři: Hua, Ziqiang, Li, Xiaorun, Feng, Yueming, Zhao, Liaoying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Convergence
Convolution
Data mining
Dual branch autoencoder (DBA)
Feature extraction
Handicrafts
hyperspectral image (HSI)
Hyperspectral imaging
Image reconstruction
Information processing
Spatial data
Spectra
spectral unmixing (SU)
spectral-spatial model
squared sine distance (SSD)
Trigonometric functions
Veins
ISSN:1545-598X, 1558-0571
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Shrnutí:Spatial information can play a supporting role in spectral unmixing. In this letter, we propose a dual branch autoencoder network to incorporate spatial-contextual information for spectral-spatial unmixing. The two branches leverage different architectures to efficiently extract spatial information and spectral information. In the first branch, we use fully connected layers to extract spectral information, where the neuron in each layer can capture all spectral features. In the second branch, 2-D convolution is adopted to exploit spatial features, which does not require hand-crafted assumptions compared with conventional methods. Then the extracted features are concatenated and propagated to generate the abundance and reconstruct the pixel. Moreover, to solve the drawbacks of the existing reconstruction functions, we propose a new function termed squared sine distance to improve the convergence quality of the proposed network. Experimental results reveal the effectiveness of our proposed method on both synthetic data and real-world data.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3091858