A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation
This paper proposes a novel hyperspectral image classification method that combines dynamic semi-supervised multiple-kernel collaborative representation ensemble selection with superpixel (SP) consistency constraints. The method is based on the consistency principle of labels within SP blocks, where...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10247512/ |
| _version_ | 1797680884683374592 |
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| author | Xiang Ge Xuexiang Yu Xu Yang |
| author_facet | Xiang Ge Xuexiang Yu Xu Yang |
| author_sort | Xiang Ge |
| collection | DOAJ |
| description | This paper proposes a novel hyperspectral image classification method that combines dynamic semi-supervised multiple-kernel collaborative representation ensemble selection with superpixel (SP) consistency constraints. The method is based on the consistency principle of labels within SP blocks, where the hyperspectral image is divided into different SP blocks, and each block is treated as an independent classification task. It applies a dynamic ensemble selection strategy to select high-confidence samples from the unlabeled data and assigns pseudo-labels to expand the available training sample set. Additionally, it employs a multiple-kernel collaborative representation classifier as the base classifier to better capture sample similarities and correlations, thereby improving the classification performance. Experimental results demonstrate that the proposed method achieves superior classification accuracy on various datasets such as Indian Pines, Purdue, and KSC, outperforming the traditional Meta-DES method significantly. |
| first_indexed | 2024-03-11T23:36:45Z |
| format | Article |
| id | doaj.art-3e0dfdcd9e1c49f293f1f26b084d7dda |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:36:45Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3e0dfdcd9e1c49f293f1f26b084d7dda2023-09-19T23:00:55ZengIEEEIEEE Access2169-35362023-01-0111988309884410.1109/ACCESS.2023.331469410247512A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP SegmentationXiang Ge0https://orcid.org/0000-0002-8235-8916Xuexiang Yu1Xu Yang2Huainan City Construction and Development Planning and Design Institute, Huainan, ChinaSchool of Spatial Information and Surveying Engineering, Anhui University of Science and Technology, Huainan, ChinaSchool of Spatial Information and Surveying Engineering, Anhui University of Science and Technology, Huainan, ChinaThis paper proposes a novel hyperspectral image classification method that combines dynamic semi-supervised multiple-kernel collaborative representation ensemble selection with superpixel (SP) consistency constraints. The method is based on the consistency principle of labels within SP blocks, where the hyperspectral image is divided into different SP blocks, and each block is treated as an independent classification task. It applies a dynamic ensemble selection strategy to select high-confidence samples from the unlabeled data and assigns pseudo-labels to expand the available training sample set. Additionally, it employs a multiple-kernel collaborative representation classifier as the base classifier to better capture sample similarities and correlations, thereby improving the classification performance. Experimental results demonstrate that the proposed method achieves superior classification accuracy on various datasets such as Indian Pines, Purdue, and KSC, outperforming the traditional Meta-DES method significantly.https://ieeexplore.ieee.org/document/10247512/Hyperspectral image classificationsuperpixel segmentationsemi-superviseddynamic classifier selectionensemble learning |
| spellingShingle | Xiang Ge Xuexiang Yu Xu Yang A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation IEEE Access Hyperspectral image classification superpixel segmentation semi-supervised dynamic classifier selection ensemble learning |
| title | A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation |
| title_full | A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation |
| title_fullStr | A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation |
| title_full_unstemmed | A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation |
| title_short | A Novel Semi-Supervised Dynamic Classifier Selection Method for HSI Classification Based on SP Segmentation |
| title_sort | novel semi supervised dynamic classifier selection method for hsi classification based on sp segmentation |
| topic | Hyperspectral image classification superpixel segmentation semi-supervised dynamic classifier selection ensemble learning |
| url | https://ieeexplore.ieee.org/document/10247512/ |
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