{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:02:21Z","timestamp":1775026941459,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research on Intelligent Monitoring and Early Warning Technology for rice pests and diseases of the Sichuan Provincial Department of Science and Technology","award":["2022NSFSC0172"],"award-info":[{"award-number":["2022NSFSC0172"]}]},{"name":"Research on Intelligent Monitoring and Early Warning Technology for rice pests and diseases of the Sichuan Provincial Department of Science and Technology","award":["202210626054"],"award-info":[{"award-number":["202210626054"]}]},{"name":"Sichuan Agricultural University Innovation Training Programme Project Funding","award":["2022NSFSC0172"],"award-info":[{"award-number":["2022NSFSC0172"]}]},{"name":"Sichuan Agricultural University Innovation Training Programme Project Funding","award":["202210626054"],"award-info":[{"award-number":["202210626054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland is highly heterogeneous and fragmented, and existing methods often suffer from inaccurate boundary segmentation. This paper introduces a UNet-like boundary-aware compensation model (BAFormer). Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Depthwise Large Kernel Multi-Layer Perceptron model (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM enhances the boundary-aware method by adaptively acquiring high-frequency features through convolution and self-attention advantages, while DWLK-MLP further supplements boundary position information using a large receptive field. The efficacy of BAFormer has been evaluated on datasets including Vaihingen, Potsdam, LoveDA, and Mapcup. It demonstrates high performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets, respectively. Notably, BAFormer-T (lightweight model) surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU.<\/jats:p>","DOI":"10.3390\/rs16142526","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T11:14:41Z","timestamp":1720610081000},"page":"2526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["BAFormer: A Novel Boundary-Aware Compensation UNet-like Transformer for High-Resolution Cropland Extraction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2402-7657","authenticated-orcid":false,"given":"Zhiyong","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3967-6365","authenticated-orcid":false,"given":"Youming","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]},{"given":"Fa","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]},{"given":"Junbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]},{"given":"Yijie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]},{"given":"Kunhong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote Sensing platforms and sensors: A survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. 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