Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

Jie Fang

doi:10.1109/LGRS.2023.3277347

Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

2023-01-01

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DOI：10.1109/LGRS.2023.3277347

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Abstract

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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.

Keywords:

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)

Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

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