Changchun Institute of Optics,Fine Mechanics and Physics,CAS
PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification | |
Q. Y. Liu; D. L. Xue; Y. H. Tang; Y. X. Zhao; J. C. Ren and H. J. Sun | |
2023 | |
发表期刊 | Remote Sensing
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卷号 | 15期号:4页码:18 |
摘要 | Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15-20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online. |
DOI | 10.3390/rs15040890 |
URL | 查看原文 |
收录类别 | sci |
语种 | 英语 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ciomp.ac.cn/handle/181722/67707 |
专题 | 中国科学院长春光学精密机械与物理研究所 |
推荐引用方式 GB/T 7714 | Q. Y. Liu,D. L. Xue,Y. H. Tang,et al. PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification[J]. Remote Sensing,2023,15(4):18. |
APA | Q. Y. Liu,D. L. Xue,Y. H. Tang,Y. X. Zhao,&J. C. Ren and H. J. Sun.(2023).PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification.Remote Sensing,15(4),18. |
MLA | Q. Y. Liu,et al."PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification".Remote Sensing 15.4(2023):18. |
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PSSA_ PCA-Domain Sup(6134KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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