{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:08:18Z","timestamp":1775326098237,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62072391"],"award-info":[{"award-number":["62072391"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62066013"],"award-info":[{"award-number":["62066013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["GGIFYTU2442"],"award-info":[{"award-number":["GGIFYTU2442"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Innovation Foundation of Yantai University (GIFYTU)","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Graduate Innovation Foundation of Yantai University (GIFYTU)","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Graduate Innovation Foundation of Yantai University (GIFYTU)","award":["GGIFYTU2442"],"award-info":[{"award-number":["GGIFYTU2442"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation is crucial for a wide range of downstream applications in remote sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The dramatic variations in grayscale and the stacking of categories within RSIs lead to unstable inter-class variance and exacerbate the uncertainty around category boundaries. However, existing methods typically emphasize spatial information while overlooking frequency insights, making it difficult to achieve desirable results. To address these challenges, we propose a novel dual-domain fusion network that integrates both spatial and frequency features. For grayscale variations, a multi-level wavelet frequency decomposition module (MWFD) is introduced to extract and integrate multi-level frequency features to enhance the distinctiveness between spatially similar categories. To mitigate the uncertainty of boundaries, a type-2 fuzzy spatial constraint module (T2FSC) is proposed to achieve flexible higher-order fuzzy modeling to adaptively constrain the boundary features in the spatial by constructing upper and lower membership functions. Furthermore, a dual-domain feature fusion (DFF) module bridges the semantic gap between the frequency and spatial features, effectively realizes semantic alignment and feature fusion between the dual domains, which further improves the accuracy of segmentation results. We conduct comprehensive experiments and extensive ablation studies on three well-known datasets: Vaihingen, Potsdam, and GID. In these three datasets, our method achieved 74.56%, 73.60%, and 81.01% mIoU, respectively. Quantitative and qualitative results demonstrate that the proposed method significantly outperforms state-of-the-art methods, achieving an excellent balance between segmentation accuracy and computational overhead.<\/jats:p>","DOI":"10.3390\/rs16193594","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T08:20:52Z","timestamp":1727338852000},"page":"3594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Dual-Domain Fusion Network Based on Wavelet Frequency Decomposition and Fuzzy Spatial Constraint for Remote Sensing Image Segmentation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6307-8273","authenticated-orcid":false,"given":"Guangyi","family":"Wei","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6688-5014","authenticated-orcid":false,"given":"Jindong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Weiqing","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Qianpeng","family":"Chong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"given":"Haihua","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}]},{"given":"Mengying","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","first-page":"102833","article-title":"Road extraction in remote sensing data: A survey","volume":"112","author":"Chen","year":"2022","journal-title":"Int. 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