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Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2023.3277347.png)
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摘要
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We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision-making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision-making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF.
Keyword:
Hyperspectral imaging
Encoding
Feature extraction
Collaboration
Convolutional neural networks
Training
Data preprocessing
Band selection (BS)
collaborative decision-making
hyperspectral image classification
spatial-spectral decoupling (SD)
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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