A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery
Hyperspectral lithological mapping is a critical task in geological surveys and mineral resource exploration. However, the distribution of geological units is typically imbalanced and heterogeneous, with practical tasks requiring high-efficiency mapping over large regions. Traditional methods, such...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003966 |
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| author | Yuanchao Wang Li He Zhengwei He Jiang Chen Fang Luo |
| author_facet | Yuanchao Wang Li He Zhengwei He Jiang Chen Fang Luo |
| author_sort | Yuanchao Wang |
| collection | DOAJ |
| description | Hyperspectral lithological mapping is a critical task in geological surveys and mineral resource exploration. However, the distribution of geological units is typically imbalanced and heterogeneous, with practical tasks requiring high-efficiency mapping over large regions. Traditional methods, such as patch-based and regular window approaches, struggle to effectively address these challenges. In this study, we propose a novel fast sample-based window lithological mapping (FSWLM) framework that integrates three key innovations: (1) a sample-based window strategy to reduce computational redundancy and improve efficiency, (2) an TFM (Threshold and Frequency Modulation)-based sampling strategy to address class imbalance during training, and (3) an encoder-decoder network characterized by feature-spectrum fusion (FSF) pattern. FSWLM offers an integrated solution tailored to the geological demands of lithological mapping, optimizing both efficiency and accuracy. Experimental validation on the Dajiling dataset, a geologically complex region in Tibet, demonstrates that FSWLM outperforms state-of-the-art methods in classification accuracy, minor class recognition, and spatial coherence. These results underscore the effectiveness of the task-oriented framework in addressing the unique challenges of hyperspectral lithological mapping, with promising implications for large-scale geological surveys and mineral resource exploration. |
| format | Article |
| id | doaj-art-2f0e5f3f6bc4434c87d9f914b0790a46 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-2f0e5f3f6bc4434c87d9f914b0790a462025-08-20T03:44:24ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-09-0114310474910.1016/j.jag.2025.104749A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imageryYuanchao Wang0Li He1Zhengwei He2Jiang Chen3Fang Luo4State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, PR China; College of Geography and Planning, Chengdu University of Technology, Chengdu 610059 Sichuan, PR China; Research Center of Applied Geology of China Geological Survey, Chengdu 610036 Sichuan, PR ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, PR China; College of Geography and Planning, Chengdu University of Technology, Chengdu 610059 Sichuan, PR China; Corresponding author at: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, No.1 Erxianqiao East Third Road, Chengdu 610059, PR China.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, PR China; College of Geography and Planning, Chengdu University of Technology, Chengdu 610059 Sichuan, PR ChinaResearch Center of Applied Geology of China Geological Survey, Chengdu 610036 Sichuan, PR ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059 Sichuan, PR ChinaHyperspectral lithological mapping is a critical task in geological surveys and mineral resource exploration. However, the distribution of geological units is typically imbalanced and heterogeneous, with practical tasks requiring high-efficiency mapping over large regions. Traditional methods, such as patch-based and regular window approaches, struggle to effectively address these challenges. In this study, we propose a novel fast sample-based window lithological mapping (FSWLM) framework that integrates three key innovations: (1) a sample-based window strategy to reduce computational redundancy and improve efficiency, (2) an TFM (Threshold and Frequency Modulation)-based sampling strategy to address class imbalance during training, and (3) an encoder-decoder network characterized by feature-spectrum fusion (FSF) pattern. FSWLM offers an integrated solution tailored to the geological demands of lithological mapping, optimizing both efficiency and accuracy. Experimental validation on the Dajiling dataset, a geologically complex region in Tibet, demonstrates that FSWLM outperforms state-of-the-art methods in classification accuracy, minor class recognition, and spatial coherence. These results underscore the effectiveness of the task-oriented framework in addressing the unique challenges of hyperspectral lithological mapping, with promising implications for large-scale geological surveys and mineral resource exploration.http://www.sciencedirect.com/science/article/pii/S1569843225003966Lithological mappingSampled-based windowFrequency modulationEncoder-decoder networkHyperspectral image |
| spellingShingle | Yuanchao Wang Li He Zhengwei He Jiang Chen Fang Luo A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery International Journal of Applied Earth Observations and Geoinformation Lithological mapping Sampled-based window Frequency modulation Encoder-decoder network Hyperspectral image |
| title | A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| title_full | A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| title_fullStr | A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| title_full_unstemmed | A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| title_short | A task-oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| title_sort | task oriented framework for efficient lithological mapping of imbalanced categories using hyperspectral imagery |
| topic | Lithological mapping Sampled-based window Frequency modulation Encoder-decoder network Hyperspectral image |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003966 |
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