Hyperspectral Image Classification Based on Pyramid Coordinate Attention and Weighted Self-Distillation

Attention mechanism-based hyperspectral image (HSI) classification algorithms typically extract spectral and spatial features by spectral attention and spatial attention network, respectively. However, these algorithms lack joint attention and ignore imbalanced samples, leading to insufficient infor...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-16
Main Authors: Shang, Ronghua, Ren, Jinhong, Zhu, Songling, Zhang, Weitong, Feng, Jie, Li, Yangyang, Jiao, Licheng
Format: Article
Language:English
Subjects:
Adaptation models
Aggregates
Algorithms
Classification
Convolution
Data mining
Distillation
Distilling
Entropy
Entropy (Information theory)
Feature extraction
Hyperspectral image (HSI) image classification
Hyperspectral imaging
Image classification
Information retrieval
Radio frequency
Residual neural networks
spatial pyramid coordinate excitation (SPCE)
spatial pyramid coordinate squeeze (SPCS)
weighted self-distillation (WSD)
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Summary:Attention mechanism-based hyperspectral image (HSI) classification algorithms typically extract spectral and spatial features by spectral attention and spatial attention network, respectively. However, these algorithms lack joint attention and ignore imbalanced samples, leading to insufficient information extraction. To address this problem, this article proposes a novel HSI classification algorithm based on the pyramidal coordinate attention and weighted self-distillation (PCA-WSD). To perform the joint attention of spectral and spatial features, the proposed PCA mechanism uses spectral attention to cope with the diverse spatial features. The PCA mechanism consists of two components. First, the spatial pyramid coordinate squeeze (SPCS) is designed to aggregate spatial features with local and global information. Then, the tailored spatial pyramid coordinate excitation (SPCE) adaptively enhances their informative spectral features for the obtained spatial features, realizing the joint attention to spectral-spatial features. Furthermore, considering the imbalance of samples, WSD is proposed. Specifically, weighted cross-entropy is integrated into WSD. Extensive experiments are evaluated on the four HSI benchmark datasets: Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), and Pavia Center (PC). Compared with the seven advanced algorithms, the experimental results of the proposed algorithm reveal superior classification performance, especially for the imbalanced samples. Our code is available at https://github.com/githubltqc/PCAWSD .
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3224604