AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau
Accurate and efficient extraction of mining areas from remote sensing imagery is essential for resource investigation, environmental assessment, and ecological management. This task is particularly crucial for the intelligent analysis of large-scale mining landscapes, such as those found on the Qing...
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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 |
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| Online Access: | https://ieeexplore.ieee.org/document/11112575/ |
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| author | Fangzhou Hong Guojin He Guizhou Wang Zhaoming Zhang Yan Peng |
| author_facet | Fangzhou Hong Guojin He Guizhou Wang Zhaoming Zhang Yan Peng |
| author_sort | Fangzhou Hong |
| collection | DOAJ |
| description | Accurate and efficient extraction of mining areas from remote sensing imagery is essential for resource investigation, environmental assessment, and ecological management. This task is particularly crucial for the intelligent analysis of large-scale mining landscapes, such as those found on the Qinghai–Tibet Plateau. However, existing approaches face notable limitations. Many deep learning models, especially those based on convolutional neural networks (CNNs), struggle to capture the complex and heterogeneous morphological features of mining areas in diverse geographic settings. To address these challenges, we propose AuxTransUNet, a hybrid deep learning framework that integrates CNNs with transformers to enhance both local detail extraction and global contextual understanding. The architecture incorporates a frequency-aware and boundary-guided feature fusion strategy, which improves segmentation accuracy by suppressing misclassification noise and refining boundary delineation. In addition, an auxiliary classification branch provides patch-level supervision, further strengthening semantic consistency. Extensive experiments conducted on a mining segmentation dataset covering the Qinghai–Tibet Plateau demonstrate that AuxTransUNet achieves superior performance compared to strong baseline models in terms of both segmentation accuracy and computational efficiency. The results highlight its potential as a robust and scalable solution for large-area mining monitoring using remote sensing data. |
| format | Article |
| id | doaj-art-91c06009be2d46f18c1909b87b3ef276 |
| 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-91c06009be2d46f18c1909b87b3ef2762025-08-22T23:09:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118203482035810.1109/JSTARS.2025.359559211112575AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet PlateauFangzhou Hong0https://orcid.org/0009-0003-6486-6444Guojin He1https://orcid.org/0009-0001-0128-8582Guizhou Wang2https://orcid.org/0000-0002-2347-8416Zhaoming Zhang3https://orcid.org/0000-0002-8779-2738Yan Peng4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAccurate and efficient extraction of mining areas from remote sensing imagery is essential for resource investigation, environmental assessment, and ecological management. This task is particularly crucial for the intelligent analysis of large-scale mining landscapes, such as those found on the Qinghai–Tibet Plateau. However, existing approaches face notable limitations. Many deep learning models, especially those based on convolutional neural networks (CNNs), struggle to capture the complex and heterogeneous morphological features of mining areas in diverse geographic settings. To address these challenges, we propose AuxTransUNet, a hybrid deep learning framework that integrates CNNs with transformers to enhance both local detail extraction and global contextual understanding. The architecture incorporates a frequency-aware and boundary-guided feature fusion strategy, which improves segmentation accuracy by suppressing misclassification noise and refining boundary delineation. In addition, an auxiliary classification branch provides patch-level supervision, further strengthening semantic consistency. Extensive experiments conducted on a mining segmentation dataset covering the Qinghai–Tibet Plateau demonstrate that AuxTransUNet achieves superior performance compared to strong baseline models in terms of both segmentation accuracy and computational efficiency. The results highlight its potential as a robust and scalable solution for large-area mining monitoring using remote sensing data.https://ieeexplore.ieee.org/document/11112575/Convolutional neural network (CNN)–transformer hybridland use mappingopen-pit miningQinghai–Tibet plateauremote sensingsemantic segmentation |
| spellingShingle | Fangzhou Hong Guojin He Guizhou Wang Zhaoming Zhang Yan Peng AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network (CNN)–transformer hybrid land use mapping open-pit mining Qinghai–Tibet plateau remote sensing semantic segmentation |
| title | AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau |
| title_full | AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau |
| title_fullStr | AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau |
| title_full_unstemmed | AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau |
| title_short | AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau |
| title_sort | auxtransunet enhancing remote sensing image segmentation of open pit mining areas in qinghai x2013 tibet plateau |
| topic | Convolutional neural network (CNN)–transformer hybrid land use mapping open-pit mining Qinghai–Tibet plateau remote sensing semantic segmentation |
| url | https://ieeexplore.ieee.org/document/11112575/ |
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