DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation
Semantic segmentation is crucial for interpreting remote sensing images. The segmentation performance has been significantly improved recently with the development of deep learning. However, complex background samples and small objects greatly increase the challenge of the semantic segmentation task...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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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/10741324/ |
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| _version_ | 1850161951105089536 |
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| author | Zhenhao Yang Fukun Bi Xinghai Hou Dehao Zhou Yanping Wang |
| author_facet | Zhenhao Yang Fukun Bi Xinghai Hou Dehao Zhou Yanping Wang |
| author_sort | Zhenhao Yang |
| collection | DOAJ |
| description | Semantic segmentation is crucial for interpreting remote sensing images. The segmentation performance has been significantly improved recently with the development of deep learning. However, complex background samples and small objects greatly increase the challenge of the semantic segmentation task for remote sensing images. To address these challenges, we propose a dual-domain refinement network (DDRNet) for accurate segmentation. Specifically, we first propose a spatial and frequency feature reconstruction module, which separately utilizes the characteristics of the frequency and spatial domains to refine the global salient features and the fine-grained spatial features of objects. This process enhances the foreground saliency and adaptively suppresses background noise. Subsequently, we propose a feature alignment module that selectively couples the features refined from both domains via cross-attention, achieving semantic alignment between frequency and spatial domains. In addition, a meticulously designed detail-aware attention module is introduced to compensate for the loss of small objects during feature propagation. This module leverages cross-correlation matrices between high-level features and the original image to quantify the similarities among objects belonging to the same category, thereby transmitting rich semantic information from high-level features to small objects. The results on multiple datasets validate that our method outperforms the existing methods and achieves a good compromise between computational overhead and accuracy. |
| format | Article |
| id | doaj-art-c4fb2e2c8e384c348a4b1eb953bcddfe |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c4fb2e2c8e384c348a4b1eb953bcddfe2025-08-20T02:22:40ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117201772018910.1109/JSTARS.2024.349058410741324DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic SegmentationZhenhao Yang0https://orcid.org/0009-0000-0641-4405Fukun Bi1https://orcid.org/0000-0003-3501-9142Xinghai Hou2Dehao Zhou3Yanping Wang4https://orcid.org/0000-0002-1287-670XSchool of Information, North China University of Technology, Beijing, ChinaSchool of Information, North China University of Technology, Beijing, ChinaSchool of Electrical and Control Engineering, North China University of Technology, Beijing, ChinaSchool of Information, North China University of Technology, Beijing, ChinaSchool of Information, North China University of Technology, Beijing, ChinaSemantic segmentation is crucial for interpreting remote sensing images. The segmentation performance has been significantly improved recently with the development of deep learning. However, complex background samples and small objects greatly increase the challenge of the semantic segmentation task for remote sensing images. To address these challenges, we propose a dual-domain refinement network (DDRNet) for accurate segmentation. Specifically, we first propose a spatial and frequency feature reconstruction module, which separately utilizes the characteristics of the frequency and spatial domains to refine the global salient features and the fine-grained spatial features of objects. This process enhances the foreground saliency and adaptively suppresses background noise. Subsequently, we propose a feature alignment module that selectively couples the features refined from both domains via cross-attention, achieving semantic alignment between frequency and spatial domains. In addition, a meticulously designed detail-aware attention module is introduced to compensate for the loss of small objects during feature propagation. This module leverages cross-correlation matrices between high-level features and the original image to quantify the similarities among objects belonging to the same category, thereby transmitting rich semantic information from high-level features to small objects. The results on multiple datasets validate that our method outperforms the existing methods and achieves a good compromise between computational overhead and accuracy.https://ieeexplore.ieee.org/document/10741324/Foreground saliency enhancementfrequency domainremote sensing segmentationsmall objects |
| spellingShingle | Zhenhao Yang Fukun Bi Xinghai Hou Dehao Zhou Yanping Wang DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Foreground saliency enhancement frequency domain remote sensing segmentation small objects |
| title | DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation |
| title_full | DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation |
| title_fullStr | DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation |
| title_full_unstemmed | DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation |
| title_short | DDRNet: Dual-Domain Refinement Network for Remote Sensing Image Semantic Segmentation |
| title_sort | ddrnet dual domain refinement network for remote sensing image semantic segmentation |
| topic | Foreground saliency enhancement frequency domain remote sensing segmentation small objects |
| url | https://ieeexplore.ieee.org/document/10741324/ |
| work_keys_str_mv | AT zhenhaoyang ddrnetdualdomainrefinementnetworkforremotesensingimagesemanticsegmentation AT fukunbi ddrnetdualdomainrefinementnetworkforremotesensingimagesemanticsegmentation AT xinghaihou ddrnetdualdomainrefinementnetworkforremotesensingimagesemanticsegmentation AT dehaozhou ddrnetdualdomainrefinementnetworkforremotesensingimagesemanticsegmentation AT yanpingwang ddrnetdualdomainrefinementnetworkforremotesensingimagesemanticsegmentation |