AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images
The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused f...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4392 |
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| author | Jiarui Li Shuli Cheng |
| author_facet | Jiarui Li Shuli Cheng |
| author_sort | Jiarui Li |
| collection | DOAJ |
| description | The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. Building upon this foundation, we develop a global multi-scale attention mechanism (GMAM) in the decoder that effectively synthesizes spatial information across multiple scales by learning comprehensive global–local relationships. The architecture is further enhanced by an efficient feature-weighted fusion module (FWFM) that systematically integrates remote spatial features with local semantic information to improve segmentation accuracy. Experimental results across diverse scenarios demonstrate that AFENet achieves superior performance in building structure detection, exhibiting enhanced segmentation connectivity and completeness compared to state-of-the-art methods. |
| format | Article |
| id | doaj-art-df3e0b92646445f096a883ea99803585 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-df3e0b92646445f096a883ea998035852024-12-13T16:30:41ZengMDPI AGRemote Sensing2072-42922024-11-011623439210.3390/rs16234392AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing ImagesJiarui Li0Shuli Cheng1School of Computer Science and Technology, Xinjiang University, Urumq 830046, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumq 830046, ChinaThe semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. Building upon this foundation, we develop a global multi-scale attention mechanism (GMAM) in the decoder that effectively synthesizes spatial information across multiple scales by learning comprehensive global–local relationships. The architecture is further enhanced by an efficient feature-weighted fusion module (FWFM) that systematically integrates remote spatial features with local semantic information to improve segmentation accuracy. Experimental results across diverse scenarios demonstrate that AFENet achieves superior performance in building structure detection, exhibiting enhanced segmentation connectivity and completeness compared to state-of-the-art methods.https://www.mdpi.com/2072-4292/16/23/4392remote sensingsemantic segmentationmulti-scale featureattention mechanism |
| spellingShingle | Jiarui Li Shuli Cheng AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images Remote Sensing remote sensing semantic segmentation multi-scale feature attention mechanism |
| title | AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images |
| title_full | AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images |
| title_fullStr | AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images |
| title_full_unstemmed | AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images |
| title_short | AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images |
| title_sort | afenet an attention focused feature enhancement network for the efficient semantic segmentation of remote sensing images |
| topic | remote sensing semantic segmentation multi-scale feature attention mechanism |
| url | https://www.mdpi.com/2072-4292/16/23/4392 |
| work_keys_str_mv | AT jiaruili afenetanattentionfocusedfeatureenhancementnetworkfortheefficientsemanticsegmentationofremotesensingimages AT shulicheng afenetanattentionfocusedfeatureenhancementnetworkfortheefficientsemanticsegmentationofremotesensingimages |