FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images
Sea-Land Segmentation (SLS) of remote sensing images is a meaningful task in the remote sensing and computer vision community. Some tricky situations, such as intraclass heterogeneity due to imaging constraints, inherent interclass similarity of sea-land regions and uncertain sea-land boundaries, st...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2024.2343531 |
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| author | Guangyi Wei Jindong Xu Qianpeng Chong Jianjun Huang |
| author_facet | Guangyi Wei Jindong Xu Qianpeng Chong Jianjun Huang |
| author_sort | Guangyi Wei |
| collection | DOAJ |
| description | Sea-Land Segmentation (SLS) of remote sensing images is a meaningful task in the remote sensing and computer vision community. Some tricky situations, such as intraclass heterogeneity due to imaging constraints, inherent interclass similarity of sea-land regions and uncertain sea-land boundaries, still are and continues to be the significant challenges in SLS. In this paper, a fuzzy-embedded multi-scale prototype network, named FMPNet, is proposed to target the above challenges of SLS task. We design a dual-branch joint attention feature extraction module (DAFM) for effective feature extraction. Memory bank (MB) is designed to collect multi-scale prototypes, aiming to obtain discriminative feature representations and guide feature selection. In addition, fuzzy connection (FC) unit is embedded in the network structure to mitigate the uncertain sea-land boundaries through 2D Gaussian fuzzy method. Extensive experimental results on a publicly SLS dataset and real region images captured by the Gaofen-1 satellite demonstrate the superior performance of the proposed FMPNet over the other state-of-the-art methods. |
| format | Article |
| id | doaj-art-ffc9368c449a49fb8ab7c0efbd332337 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-ffc9368c449a49fb8ab7c0efbd3323372025-08-20T02:33:44ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2024.2343531FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing imagesGuangyi Wei0Jindong Xu1Qianpeng Chong2Jianjun Huang3School of Computer and Control Engineering, Yantai University, Yantai, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, ChinaSea-Land Segmentation (SLS) of remote sensing images is a meaningful task in the remote sensing and computer vision community. Some tricky situations, such as intraclass heterogeneity due to imaging constraints, inherent interclass similarity of sea-land regions and uncertain sea-land boundaries, still are and continues to be the significant challenges in SLS. In this paper, a fuzzy-embedded multi-scale prototype network, named FMPNet, is proposed to target the above challenges of SLS task. We design a dual-branch joint attention feature extraction module (DAFM) for effective feature extraction. Memory bank (MB) is designed to collect multi-scale prototypes, aiming to obtain discriminative feature representations and guide feature selection. In addition, fuzzy connection (FC) unit is embedded in the network structure to mitigate the uncertain sea-land boundaries through 2D Gaussian fuzzy method. Extensive experimental results on a publicly SLS dataset and real region images captured by the Gaofen-1 satellite demonstrate the superior performance of the proposed FMPNet over the other state-of-the-art methods.https://www.tandfonline.com/doi/10.1080/22797254.2024.2343531Fuzzy methodmulti-scale prototyperemote sensing imagessea land segmentation |
| spellingShingle | Guangyi Wei Jindong Xu Qianpeng Chong Jianjun Huang FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images European Journal of Remote Sensing Fuzzy method multi-scale prototype remote sensing images sea land segmentation |
| title | FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images |
| title_full | FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images |
| title_fullStr | FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images |
| title_full_unstemmed | FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images |
| title_short | FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images |
| title_sort | fmpnet a fuzzy embedded multi scale prototype network for sea land segmentation of remote sensing images |
| topic | Fuzzy method multi-scale prototype remote sensing images sea land segmentation |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2024.2343531 |
| work_keys_str_mv | AT guangyiwei fmpnetafuzzyembeddedmultiscaleprototypenetworkforsealandsegmentationofremotesensingimages AT jindongxu fmpnetafuzzyembeddedmultiscaleprototypenetworkforsealandsegmentationofremotesensingimages AT qianpengchong fmpnetafuzzyembeddedmultiscaleprototypenetworkforsealandsegmentationofremotesensingimages AT jianjunhuang fmpnetafuzzyembeddedmultiscaleprototypenetworkforsealandsegmentationofremotesensingimages |