Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering
Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correla...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/193 |
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| author | Junbin Zhuang Wenying Chen Xunan Huang Yunyi Yan |
| author_facet | Junbin Zhuang Wenying Chen Xunan Huang Yunyi Yan |
| author_sort | Junbin Zhuang |
| collection | DOAJ |
| description | Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. |
| format | Article |
| id | doaj-art-177a414618924eed91f168bb52bf451b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-177a414618924eed91f168bb52bf451b2025-01-24T13:47:41ZengMDPI AGRemote Sensing2072-42922025-01-0117219310.3390/rs17020193Band Selection Algorithm Based on Multi-Feature and Affinity Propagation ClusteringJunbin Zhuang0Wenying Chen1Xunan Huang2Yunyi Yan3School of Aerospace Science and Technology, Xidian University, Xi’an 710126, ChinaSchool of Aerospace Science and Technology, Xidian University, Xi’an 710126, ChinaAir Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, ChinaSchool of Aerospace Science and Technology, Xidian University, Xi’an 710126, ChinaHyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability.https://www.mdpi.com/2072-4292/17/2/193hyperspectral imagesdimensionality reductionband selection methodsimilarity matrix |
| spellingShingle | Junbin Zhuang Wenying Chen Xunan Huang Yunyi Yan Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering Remote Sensing hyperspectral images dimensionality reduction band selection method similarity matrix |
| title | Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering |
| title_full | Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering |
| title_fullStr | Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering |
| title_full_unstemmed | Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering |
| title_short | Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering |
| title_sort | band selection algorithm based on multi feature and affinity propagation clustering |
| topic | hyperspectral images dimensionality reduction band selection method similarity matrix |
| url | https://www.mdpi.com/2072-4292/17/2/193 |
| work_keys_str_mv | AT junbinzhuang bandselectionalgorithmbasedonmultifeatureandaffinitypropagationclustering AT wenyingchen bandselectionalgorithmbasedonmultifeatureandaffinitypropagationclustering AT xunanhuang bandselectionalgorithmbasedonmultifeatureandaffinitypropagationclustering AT yunyiyan bandselectionalgorithmbasedonmultifeatureandaffinitypropagationclustering |