Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images
Salient Object Detection (SOD), an important preprocessing part of image processing, identifies and labels the most attention-grabbing objects by simulating human vision. Because remote sensing images (RSIs) have different characteristics from natural images such as the limitation of shooting angle...
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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/10979684/ |
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| author | Weining Zhai Liejun Wang Panpan Zheng Lele Li |
| author_facet | Weining Zhai Liejun Wang Panpan Zheng Lele Li |
| author_sort | Weining Zhai |
| collection | DOAJ |
| description | Salient Object Detection (SOD), an important preprocessing part of image processing, identifies and labels the most attention-grabbing objects by simulating human vision. Because remote sensing images (RSIs) have different characteristics from natural images such as the limitation of shooting angle and variable scale. RSI-SOD often faces problems such as incomplete structure, missing semantic information, and blurred edges. Our Multilevel Complementary Cooperative Network (MCoCoNet) is capable of balancing semantic and detailed information to reduce noise interference to ensure semantic integrity through feature fusion in a multi-channel aligned manner. And it is adapted to the network requirements for more targeted feature extraction. Specifically, the Neighbourhood Feature Co-Extractor (NFCoE) is designed between the encoder and the decoder to utilize features from neighbouring layers to complement the missing semantic information as well as the detail information within the current layer, thus ensuring the integrity of the structure. The Parallel Refinement Block (PRB), as a decoder, which is combined with contextual information to gradually refine the target edges. It is shown by extensive experiments and visualisations that MCoCoNet provides new improvement ideas for existing RSI-SOD models. |
| format | Article |
| id | doaj-art-bf4c3beec7b04a6d8fa4e9f4db50bbd3 |
| 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-bf4c3beec7b04a6d8fa4e9f4db50bbd32025-08-20T01:56:48ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118127401275410.1109/JSTARS.2025.356359110979684Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing ImagesWeining Zhai0https://orcid.org/0009-0007-9387-7993Liejun Wang1https://orcid.org/0000-0003-0210-2273Panpan Zheng2https://orcid.org/0009-0003-2934-6339Lele Li3https://orcid.org/0009-0002-1775-4506School of Computer Science and Technology, Xinjiang University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang University, Ürümqi, ChinaSalient Object Detection (SOD), an important preprocessing part of image processing, identifies and labels the most attention-grabbing objects by simulating human vision. Because remote sensing images (RSIs) have different characteristics from natural images such as the limitation of shooting angle and variable scale. RSI-SOD often faces problems such as incomplete structure, missing semantic information, and blurred edges. Our Multilevel Complementary Cooperative Network (MCoCoNet) is capable of balancing semantic and detailed information to reduce noise interference to ensure semantic integrity through feature fusion in a multi-channel aligned manner. And it is adapted to the network requirements for more targeted feature extraction. Specifically, the Neighbourhood Feature Co-Extractor (NFCoE) is designed between the encoder and the decoder to utilize features from neighbouring layers to complement the missing semantic information as well as the detail information within the current layer, thus ensuring the integrity of the structure. The Parallel Refinement Block (PRB), as a decoder, which is combined with contextual information to gradually refine the target edges. It is shown by extensive experiments and visualisations that MCoCoNet provides new improvement ideas for existing RSI-SOD models.https://ieeexplore.ieee.org/document/10979684/Multi-Scale feature fusionoptical remote sensing images (RSIs)salient object detection (SOD)three-channel coordinated attention (TCCA) |
| spellingShingle | Weining Zhai Liejun Wang Panpan Zheng Lele Li Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Multi-Scale feature fusion optical remote sensing images (RSIs) salient object detection (SOD) three-channel coordinated attention (TCCA) |
| title | Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images |
| title_full | Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images |
| title_fullStr | Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images |
| title_full_unstemmed | Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images |
| title_short | Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images |
| title_sort | multichannel aligned feature fusion method for salient object detection in optical remote sensing images |
| topic | Multi-Scale feature fusion optical remote sensing images (RSIs) salient object detection (SOD) three-channel coordinated attention (TCCA) |
| url | https://ieeexplore.ieee.org/document/10979684/ |
| work_keys_str_mv | AT weiningzhai multichannelalignedfeaturefusionmethodforsalientobjectdetectioninopticalremotesensingimages AT liejunwang multichannelalignedfeaturefusionmethodforsalientobjectdetectioninopticalremotesensingimages AT panpanzheng multichannelalignedfeaturefusionmethodforsalientobjectdetectioninopticalremotesensingimages AT leleli multichannelalignedfeaturefusionmethodforsalientobjectdetectioninopticalremotesensingimages |