Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images
Although linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based subspace learning methods exhibit several limitations: (1) They are often sensitive to noise and demonstrate weak robustness; (2) these meth...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| author | Cong-Yin Cao Meng-Ting Li Yang-Jun Deng Longfei Ren Yi Liu Xing-Hui Zhu |
| author_facet | Cong-Yin Cao Meng-Ting Li Yang-Jun Deng Longfei Ren Yi Liu Xing-Hui Zhu |
| author_sort | Cong-Yin Cao |
| collection | DOAJ |
| description | Although linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based subspace learning methods exhibit several limitations: (1) They are often sensitive to noise and demonstrate weak robustness; (2) these methods ignore the local information inherent in data; and (3) the number of extracted features is restricted by the number of classes. To address these drawbacks, this paper proposes a novel joint sparse local linear discriminant analysis (JSLLDA) method by integrating embedding regression and locality-preserving regularization into the LDA model for feature dimensionality reduction of HSIs. In JSLLDA, a row-sparse projection matrix can be learned, to uncover the joint sparse structure information of data by imposing a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm constraint. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm is also employed to measure the embedding regression reconstruction error, thereby mitigating the effects of noise and occlusions. A locality preservation term is incorporated to fully leverage the local geometric structural information of the data, enhancing the discriminability of the learned projection. Furthermore, an orthogonal matrix is introduced to alleviate the limitation on the number of acquired features. Finally, extensive experiments conducted on three hyperspectral image (HSI) datasets demonstrated that the performance of JSLLDA surpassed that of some related state-of-the-art dimensionality reduction methods. |
| format | Article |
| id | doaj-art-28a706bf2b5345a1bbcbf98aaaeaa203 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-28a706bf2b5345a1bbcbf98aaaeaa2032025-08-20T01:53:56ZengMDPI AGRemote Sensing2072-42922024-11-011622428710.3390/rs16224287Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral ImagesCong-Yin Cao0Meng-Ting Li1Yang-Jun Deng2Longfei Ren3Yi Liu4Xing-Hui Zhu5College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaKey Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaAlthough linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based subspace learning methods exhibit several limitations: (1) They are often sensitive to noise and demonstrate weak robustness; (2) these methods ignore the local information inherent in data; and (3) the number of extracted features is restricted by the number of classes. To address these drawbacks, this paper proposes a novel joint sparse local linear discriminant analysis (JSLLDA) method by integrating embedding regression and locality-preserving regularization into the LDA model for feature dimensionality reduction of HSIs. In JSLLDA, a row-sparse projection matrix can be learned, to uncover the joint sparse structure information of data by imposing a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm constraint. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm is also employed to measure the embedding regression reconstruction error, thereby mitigating the effects of noise and occlusions. A locality preservation term is incorporated to fully leverage the local geometric structural information of the data, enhancing the discriminability of the learned projection. Furthermore, an orthogonal matrix is introduced to alleviate the limitation on the number of acquired features. Finally, extensive experiments conducted on three hyperspectral image (HSI) datasets demonstrated that the performance of JSLLDA surpassed that of some related state-of-the-art dimensionality reduction methods.https://www.mdpi.com/2072-4292/16/22/4287hyperspectral image (HSI)dimensionality reductionlinear discriminant analysisembedding regression regularization |
| spellingShingle | Cong-Yin Cao Meng-Ting Li Yang-Jun Deng Longfei Ren Yi Liu Xing-Hui Zhu Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images Remote Sensing hyperspectral image (HSI) dimensionality reduction linear discriminant analysis embedding regression regularization |
| title | Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images |
| title_full | Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images |
| title_fullStr | Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images |
| title_full_unstemmed | Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images |
| title_short | Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images |
| title_sort | joint sparse local linear discriminant analysis for feature dimensionality reduction of hyperspectral images |
| topic | hyperspectral image (HSI) dimensionality reduction linear discriminant analysis embedding regression regularization |
| url | https://www.mdpi.com/2072-4292/16/22/4287 |
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