Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909420700565504 |
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| author | Ren, Yuemei Liao, Liang Maybank, Stephen John Zhang, Yanning Liu, Xin |
| author_facet | Ren, Yuemei Liao, Liang Maybank, Stephen John Zhang, Yanning Liu, Xin |
| contents | This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditional Principal Component Analysis (PCA) technique is extended to its tensor-based counterpart, referred to as Tensor Principal Component Analysis (TPCA). The proposed TPCA method leverages the inherent multi-dimensional structure of hyperspectral data, thereby enabling more effective feature representation. Experimental results on benchmark hyperspectral datasets demonstrate that classification models using TPCA features consistently outperform those using traditional PCA and other state-of-the-art techniques. These findings highlight the potential of the tensor-based framework in advancing hyperspectral image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06075 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis Ren, Yuemei Liao, Liang Maybank, Stephen John Zhang, Yanning Liu, Xin Computer Vision and Pattern Recognition Machine Learning This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditional Principal Component Analysis (PCA) technique is extended to its tensor-based counterpart, referred to as Tensor Principal Component Analysis (TPCA). The proposed TPCA method leverages the inherent multi-dimensional structure of hyperspectral data, thereby enabling more effective feature representation. Experimental results on benchmark hyperspectral datasets demonstrate that classification models using TPCA features consistently outperform those using traditional PCA and other state-of-the-art techniques. These findings highlight the potential of the tensor-based framework in advancing hyperspectral image analysis. |
| title | Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.06075 |