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ABSTRACT
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.
Acknowledgments
All authors would sincerely thank the reviewers and editors for their suggestions and opinions for improving this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The SLSD dataset is available at https://github.com/lllltdaf2/Sea-land-segmentation-data; the field data for the Jiaodong Peninsula from Gaofen-1 was provided by our work department, and some of them are publicly available at https://github.com/CVFishwgy/Gaofen1_sea_land_data.