Enhancing Building Segmentation With Shadow-Aware Edge Perception
Accurate building semantic segmentation in remote sensing imagery is essential for urban planning, environmental monitoring, and map creation. While deep learning has achieved significant advancements in this field, precisely segmenting building edges and shadows in complex scenarios remains challen...
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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/10713904/ |
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| _version_ | 1850267890693963776 |
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| author | Ying Yu Chunping Wang Renke Kou Huiying Wang Boxiong Yang Jinhui Xu Qiang Fu |
| author_facet | Ying Yu Chunping Wang Renke Kou Huiying Wang Boxiong Yang Jinhui Xu Qiang Fu |
| author_sort | Ying Yu |
| collection | DOAJ |
| description | Accurate building semantic segmentation in remote sensing imagery is essential for urban planning, environmental monitoring, and map creation. While deep learning has achieved significant advancements in this field, precisely segmenting building edges and shadows in complex scenarios remains challenging. Shadows often introduce boundary ambiguities, affecting the local shape and texture information of buildings. Current methods do not fully perceive or utilize shadows. To address these challenges, we propose an advanced high-resolution image segmentation network, high-resolution network, integrated with a shadow-inclusive edge perception module. Our approach involves introducing a shadow-inclusive contour transition module (SCTM) during the feature extraction stage to enhance the features of blurry boundaries. The proposed SCTM and shadow-aware attention module significantly enhance attention maps, improve responses in blurry boundary regions, and increase consistency between predictions and ground truth, setting a new benchmark for building semantic segmentation in remote sensing imagery. This enriched information is then fed into an attention module that concurrently focuses on boundary and channel features, surpassing traditional semantic segmentation methods. We validated our method on three datasets: Massachusetts, WHU, and Inria. Our approach outperformed state-of-the-art methods on the WHU Building Dataset across all metrics, including mIoU, Accuracy, Kappa, and Dice coefficient. |
| format | Article |
| id | doaj-art-d4a4c56b5c954955b2856cb6f0241864 |
| institution | OA Journals |
| 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-d4a4c56b5c954955b2856cb6f02418642025-08-20T01:53:36ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011811210.1109/JSTARS.2024.347907310713904Enhancing Building Segmentation With Shadow-Aware Edge PerceptionYing Yu0https://orcid.org/0000-0001-7840-9891Chunping Wang1Renke Kou2https://orcid.org/0000-0001-5893-3127Huiying Wang3https://orcid.org/0009-0004-0346-3585Boxiong Yang4Jinhui Xu5Qiang Fu6https://orcid.org/0000-0002-3831-9856Shijiazhuang campus, Army Engineering University of PLA, Shijiazhuang, ChinaShijiazhuang campus, Army Engineering University of PLA, Shijiazhuang, ChinaSchool of Aviation Engineering, Air Force Engineering University, Xi'an, ChinaShijiazhuang campus, Army Engineering University of PLA, Shijiazhuang, ChinaSchool of Information and Intelligent Engineering, University of Sanya, Sanya, ChinaSchool of Information and Intelligent Engineering, University of Sanya, Sanya, ChinaShijiazhuang campus, Army Engineering University of PLA, Shijiazhuang, ChinaAccurate building semantic segmentation in remote sensing imagery is essential for urban planning, environmental monitoring, and map creation. While deep learning has achieved significant advancements in this field, precisely segmenting building edges and shadows in complex scenarios remains challenging. Shadows often introduce boundary ambiguities, affecting the local shape and texture information of buildings. Current methods do not fully perceive or utilize shadows. To address these challenges, we propose an advanced high-resolution image segmentation network, high-resolution network, integrated with a shadow-inclusive edge perception module. Our approach involves introducing a shadow-inclusive contour transition module (SCTM) during the feature extraction stage to enhance the features of blurry boundaries. The proposed SCTM and shadow-aware attention module significantly enhance attention maps, improve responses in blurry boundary regions, and increase consistency between predictions and ground truth, setting a new benchmark for building semantic segmentation in remote sensing imagery. This enriched information is then fed into an attention module that concurrently focuses on boundary and channel features, surpassing traditional semantic segmentation methods. We validated our method on three datasets: Massachusetts, WHU, and Inria. Our approach outperformed state-of-the-art methods on the WHU Building Dataset across all metrics, including mIoU, Accuracy, Kappa, and Dice coefficient.https://ieeexplore.ieee.org/document/10713904/Buildings segmentationremote sensingsemantic segmentationshadow-aware attention moduleshadow-inclusive contour transition module (SCTM) |
| spellingShingle | Ying Yu Chunping Wang Renke Kou Huiying Wang Boxiong Yang Jinhui Xu Qiang Fu Enhancing Building Segmentation With Shadow-Aware Edge Perception IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Buildings segmentation remote sensing semantic segmentation shadow-aware attention module shadow-inclusive contour transition module (SCTM) |
| title | Enhancing Building Segmentation With Shadow-Aware Edge Perception |
| title_full | Enhancing Building Segmentation With Shadow-Aware Edge Perception |
| title_fullStr | Enhancing Building Segmentation With Shadow-Aware Edge Perception |
| title_full_unstemmed | Enhancing Building Segmentation With Shadow-Aware Edge Perception |
| title_short | Enhancing Building Segmentation With Shadow-Aware Edge Perception |
| title_sort | enhancing building segmentation with shadow aware edge perception |
| topic | Buildings segmentation remote sensing semantic segmentation shadow-aware attention module shadow-inclusive contour transition module (SCTM) |
| url | https://ieeexplore.ieee.org/document/10713904/ |
| work_keys_str_mv | AT yingyu enhancingbuildingsegmentationwithshadowawareedgeperception AT chunpingwang enhancingbuildingsegmentationwithshadowawareedgeperception AT renkekou enhancingbuildingsegmentationwithshadowawareedgeperception AT huiyingwang enhancingbuildingsegmentationwithshadowawareedgeperception AT boxiongyang enhancingbuildingsegmentationwithshadowawareedgeperception AT jinhuixu enhancingbuildingsegmentationwithshadowawareedgeperception AT qiangfu enhancingbuildingsegmentationwithshadowawareedgeperception |