Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost
Band selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications. eXtreme Gradient Boosting (XGBoost), an interpretable tree-based ensemble learning classifier, explicitly implements the complex nonlinear hypers...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11008687/ |
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| author | Xiaodi Shang Chuanyu Cui Xudong Sun Xiaopeng Wang Jiahua Zhang |
| author_facet | Xiaodi Shang Chuanyu Cui Xudong Sun Xiaopeng Wang Jiahua Zhang |
| author_sort | Xiaodi Shang |
| collection | DOAJ |
| description | Band selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications. eXtreme Gradient Boosting (XGBoost), an interpretable tree-based ensemble learning classifier, explicitly implements the complex nonlinear hyperspectral classification. The interpretable information extracted from the tree structure offers a novel basis for supervised BS. To this end, this article proposes a supervised BS method, named classification task-driven hyperspectral BS via interpretability from XGBoost (XGBS). It leverages prior knowledge to train a classification task-driven XGBoost and interprets the tree structure to extract multivariate interpretable information, encompassing band split gain and two types of band dependencies. Subsequently, a heuristic search framework is employed to evaluate band performance, facilitating the selection of bands with strong classification capability, high interdependency, and low redundancy. Experiments conducted on six real HSI datasets demonstrate the effectiveness and stability of the proposed XGBS. |
| format | Article |
| id | doaj-art-8f3f1a9cd4264366b0a11b97a9cf9b1d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8f3f1a9cd4264366b0a11b97a9cf9b1d2025-08-20T03:26:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118137331375410.1109/JSTARS.2025.357227811008687Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoostXiaodi Shang0https://orcid.org/0000-0002-0133-8447Chuanyu Cui1https://orcid.org/0009-0000-5479-6938Xudong Sun2https://orcid.org/0000-0002-5870-6343Xiaopeng Wang3https://orcid.org/0009-0005-8965-7758Jiahua Zhang4https://orcid.org/0000-0002-2894-9627College of Computer Science and Technology, Qingdao University, Qingdao, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaBand selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications. eXtreme Gradient Boosting (XGBoost), an interpretable tree-based ensemble learning classifier, explicitly implements the complex nonlinear hyperspectral classification. The interpretable information extracted from the tree structure offers a novel basis for supervised BS. To this end, this article proposes a supervised BS method, named classification task-driven hyperspectral BS via interpretability from XGBoost (XGBS). It leverages prior knowledge to train a classification task-driven XGBoost and interprets the tree structure to extract multivariate interpretable information, encompassing band split gain and two types of band dependencies. Subsequently, a heuristic search framework is employed to evaluate band performance, facilitating the selection of bands with strong classification capability, high interdependency, and low redundancy. Experiments conducted on six real HSI datasets demonstrate the effectiveness and stability of the proposed XGBS.https://ieeexplore.ieee.org/document/11008687/Band dependenceclassification task-driven band selection (BS)eXtreme Gradient Boosting (XGBoost)hyperspectral imagery (HSI)multivariate interpretable information (MII) |
| spellingShingle | Xiaodi Shang Chuanyu Cui Xudong Sun Xiaopeng Wang Jiahua Zhang Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Band dependence classification task-driven band selection (BS) eXtreme Gradient Boosting (XGBoost) hyperspectral imagery (HSI) multivariate interpretable information (MII) |
| title | Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost |
| title_full | Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost |
| title_fullStr | Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost |
| title_full_unstemmed | Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost |
| title_short | Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost |
| title_sort | classification task driven hyperspectral band selection via interpretability from xgboost |
| topic | Band dependence classification task-driven band selection (BS) eXtreme Gradient Boosting (XGBoost) hyperspectral imagery (HSI) multivariate interpretable information (MII) |
| url | https://ieeexplore.ieee.org/document/11008687/ |
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