Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
We consider the tensor-based spectral-spatial feature extraction problem for hyperspectral image classification. First, a tensor framework based on circular convolution is proposed. Based on this framework, we extend the traditional principal component analysis (PCA) to its tensorial version tensor...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2017-09, Vol.14 (9), p.1431-1435 |
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| creator | Yuemei Ren Liang Liao Maybank, Stephen John Yanning Zhang Xin Liu |
| description | We consider the tensor-based spectral-spatial feature extraction problem for hyperspectral image classification. First, a tensor framework based on circular convolution is proposed. Based on this framework, we extend the traditional principal component analysis (PCA) to its tensorial version tensor PCA (TPCA), which is applied to the spectral-spatial features of hyperspectral image data. The experiments show that the classification accuracy obtained using TPCA features is significantly higher than the accuracies obtained by its rivals. |
| doi_str_mv | 10.1109/LGRS.2017.2686878 |
| format | Article |
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First, a tensor framework based on circular convolution is proposed. Based on this framework, we extend the traditional principal component analysis (PCA) to its tensorial version tensor PCA (TPCA), which is applied to the spectral-spatial features of hyperspectral image data. 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| ispartof | IEEE geoscience and remote sensing letters, 2017-09, Vol.14 (9), p.1431-1435 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Classification Convolution Feature extraction Fourier transforms Frameworks hyperspectral image classification Hyperspectral imaging Image classification Mathematical analysis Matrix decomposition Principal component analysis principal component analysis (PCA) Principal components analysis Spectra Tensile stress tensor model Tensors |
| title | Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis |
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