A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas
To address complex semantic segmentation in coal mine areas, this study proposes the SAM-SEF (SAM-based Semantic Enhancement Framework). It integrates Semantic-SAM’s zero-shot segmentation capability with specialized deep learning models through a “segmentation-classification-u...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11023034/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849421440025100288 |
|---|---|
| author | Libing Wang Jihong Dong Lei Wang Feng Liu Qingke Wen Bekir Genc |
| author_facet | Libing Wang Jihong Dong Lei Wang Feng Liu Qingke Wen Bekir Genc |
| author_sort | Libing Wang |
| collection | DOAJ |
| description | To address complex semantic segmentation in coal mine areas, this study proposes the SAM-SEF (SAM-based Semantic Enhancement Framework). It integrates Semantic-SAM’s zero-shot segmentation capability with specialized deep learning models through a “segmentation-classification-update” strategy. Semantic-SAM initially provides category-agnostic multi-granularity segmentations; specialized deep learning models then perform semantic understanding of these regions, and a category mapping mechanism generates precise results. Evaluating 18 models, SAM-SEF-ENet-B0 achieved the highest IoUs for Open-pit Coal Mining Sites (OCMS) (93.68%) and Composite Coal-related Sites (CCS) (81.52%), with an mIoU of 87.60%, significantly outperforming traditional models. A Semantic-SAM-based semi-automatic annotation tool reduced manual annotation time by approximately 60%. A multi-scale evaluation system (based on 33% and 66% target area percentiles) revealed a positive correlation between target scale and segmentation performance, with small-scale target segmentation being the primary challenge (IoUs of 0.79 for OCMS and 0.67 for CCS in small scales). Systematic experiments validated Semantic-SAM’s superiority over SAM (mIoU 54.03%) and SAM2 (mIoU 45.33%) in semantic understanding, architectural adaptability, and computational efficiency. The study also found that classification networks within the SAM-SEF framework outperformed specially designed segmentation networks. This demonstrates the effectiveness of a “classification-driven segmentation” paradigm, where lightweight classifiers operating on purified targets from a frozen foundation model can surpass traditional end-to-end segmentation networks in specific, complex domains, offering new technical solutions for remote sensing monitoring. |
| format | Article |
| id | doaj-art-9cc225b59a3244e5a1822c2148b0f7e2 |
| 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-9cc225b59a3244e5a1822c2148b0f7e22025-08-20T03:31:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118148201484210.1109/JSTARS.2025.357628511023034A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine AreasLibing Wang0https://orcid.org/0000-0001-8382-8425Jihong Dong1https://orcid.org/0009-0004-7851-3276Lei Wang2Feng Liu3Qingke Wen4Bekir Genc5https://orcid.org/0000-0002-3943-5103School of Environment and Spatial Informatics, China University of Mining and Technology and Engineering Research Center of Mine Ecological Restoration, Ministry of Education, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaChina Coal Society, Beijing, ChinaChina Coal Society, Beijing, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSchool of Mining Engineering, University of the Witwatersrand, Johannesburg, South AfricaTo address complex semantic segmentation in coal mine areas, this study proposes the SAM-SEF (SAM-based Semantic Enhancement Framework). It integrates Semantic-SAM’s zero-shot segmentation capability with specialized deep learning models through a “segmentation-classification-update” strategy. Semantic-SAM initially provides category-agnostic multi-granularity segmentations; specialized deep learning models then perform semantic understanding of these regions, and a category mapping mechanism generates precise results. Evaluating 18 models, SAM-SEF-ENet-B0 achieved the highest IoUs for Open-pit Coal Mining Sites (OCMS) (93.68%) and Composite Coal-related Sites (CCS) (81.52%), with an mIoU of 87.60%, significantly outperforming traditional models. A Semantic-SAM-based semi-automatic annotation tool reduced manual annotation time by approximately 60%. A multi-scale evaluation system (based on 33% and 66% target area percentiles) revealed a positive correlation between target scale and segmentation performance, with small-scale target segmentation being the primary challenge (IoUs of 0.79 for OCMS and 0.67 for CCS in small scales). Systematic experiments validated Semantic-SAM’s superiority over SAM (mIoU 54.03%) and SAM2 (mIoU 45.33%) in semantic understanding, architectural adaptability, and computational efficiency. The study also found that classification networks within the SAM-SEF framework outperformed specially designed segmentation networks. This demonstrates the effectiveness of a “classification-driven segmentation” paradigm, where lightweight classifiers operating on purified targets from a frozen foundation model can surpass traditional end-to-end segmentation networks in specific, complex domains, offering new technical solutions for remote sensing monitoring.https://ieeexplore.ieee.org/document/11023034/Coal mine site segmentationdeep learningmultiscale evaluationremote sensing image segmentationsemantic-SAMsemi-automatic annotation |
| spellingShingle | Libing Wang Jihong Dong Lei Wang Feng Liu Qingke Wen Bekir Genc A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coal mine site segmentation deep learning multiscale evaluation remote sensing image segmentation semantic-SAM semi-automatic annotation |
| title | A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas |
| title_full | A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas |
| title_fullStr | A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas |
| title_full_unstemmed | A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas |
| title_short | A Decoupled Segmentation-Classification Strategy Based on Semantic-SAM for Precise Semantic Segmentation in Coal Mine Areas |
| title_sort | decoupled segmentation classification strategy based on semantic sam for precise semantic segmentation in coal mine areas |
| topic | Coal mine site segmentation deep learning multiscale evaluation remote sensing image segmentation semantic-SAM semi-automatic annotation |
| url | https://ieeexplore.ieee.org/document/11023034/ |
| work_keys_str_mv | AT libingwang adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT jihongdong adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT leiwang adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT fengliu adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT qingkewen adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT bekirgenc adecoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT libingwang decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT jihongdong decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT leiwang decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT fengliu decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT qingkewen decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas AT bekirgenc decoupledsegmentationclassificationstrategybasedonsemanticsamforprecisesemanticsegmentationincoalmineareas |