Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery
Band selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective. However, most of the existing methods select b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5679 |
| _version_ | 1797464129116569600 |
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| author | Xudong Sun Xin Shen Huijuan Pang Xianping Fu |
| author_facet | Xudong Sun Xin Shen Huijuan Pang Xianping Fu |
| author_sort | Xudong Sun |
| collection | DOAJ |
| description | Band selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective. However, most of the existing methods select bands according to a single criterion, leading to incomplete band evaluation and insufficient generalization against different data sets. To address this problem, this work proposes a multi-criteria-based band selection (MCBS) framework, which innovatively treats BS as a multi-criteria decision-making (MCDM) problem. First, a decision matrix is constructed based on several typical BPs, so as to evaluate the bands from different focuses. Then, MCBS defines the global positive and negative idea solutions and selects bands according to their relative closeness to these solutions. Since each BP has a different capability to discriminate the bands, two weight estimation approaches are developed to adaptively balance the contributions of various criteria. Finally, this work also provides an extended version of MCBS, which incorporates the subspace partition strategy to reduce the correlation of the selected bands. In this paper, the classification task is used to evaluate the performance of the selected band subsets. Extensive experiments on three public data sets verify that the proposed method outperforms other state-of-the-art methods. |
| first_indexed | 2024-03-09T18:02:34Z |
| format | Article |
| id | doaj.art-fc23fd1d8ca5492595970e53ff895297 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fc23fd1d8ca5492595970e53ff8952972023-11-24T09:48:42ZengMDPI AGRemote Sensing2072-42922022-11-011422567910.3390/rs14225679Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral ImageryXudong Sun0Xin Shen1Huijuan Pang2Xianping Fu3School of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaBand selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective. However, most of the existing methods select bands according to a single criterion, leading to incomplete band evaluation and insufficient generalization against different data sets. To address this problem, this work proposes a multi-criteria-based band selection (MCBS) framework, which innovatively treats BS as a multi-criteria decision-making (MCDM) problem. First, a decision matrix is constructed based on several typical BPs, so as to evaluate the bands from different focuses. Then, MCBS defines the global positive and negative idea solutions and selects bands according to their relative closeness to these solutions. Since each BP has a different capability to discriminate the bands, two weight estimation approaches are developed to adaptively balance the contributions of various criteria. Finally, this work also provides an extended version of MCBS, which incorporates the subspace partition strategy to reduce the correlation of the selected bands. In this paper, the classification task is used to evaluate the performance of the selected band subsets. Extensive experiments on three public data sets verify that the proposed method outperforms other state-of-the-art methods.https://www.mdpi.com/2072-4292/14/22/5679hyperspectral imageband selectionband prioritizationsubspace partitionmultiple criteria-based band selection (MCBS) |
| spellingShingle | Xudong Sun Xin Shen Huijuan Pang Xianping Fu Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery Remote Sensing hyperspectral image band selection band prioritization subspace partition multiple criteria-based band selection (MCBS) |
| title | Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery |
| title_full | Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery |
| title_fullStr | Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery |
| title_full_unstemmed | Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery |
| title_short | Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery |
| title_sort | multiple band prioritization criteria based band selection for hyperspectral imagery |
| topic | hyperspectral image band selection band prioritization subspace partition multiple criteria-based band selection (MCBS) |
| url | https://www.mdpi.com/2072-4292/14/22/5679 |
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