{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:31:40Z","timestamp":1778085100283,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T00:00:00Z","timestamp":1662681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61901307"],"award-info":[{"award-number":["61901307"]}]},{"name":"National Natural Science Foundation of China","award":["20E01"],"award-info":[{"award-number":["20E01"]}]},{"name":"National Natural Science Foundation of China","award":["BSQD2020054"],"award-info":[{"award-number":["BSQD2020054"]}]},{"name":"National Natural Science Foundation of China","award":["BSQD2020055"],"award-info":[{"award-number":["BSQD2020055"]}]},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["61901307"],"award-info":[{"award-number":["61901307"]}]},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["20E01"],"award-info":[{"award-number":["20E01"]}]},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["BSQD2020054"],"award-info":[{"award-number":["BSQD2020054"]}]},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["BSQD2020055"],"award-info":[{"award-number":["BSQD2020055"]}]},{"name":"Scientific Research Foundation","award":["61901307"],"award-info":[{"award-number":["61901307"]}]},{"name":"Scientific Research Foundation","award":["20E01"],"award-info":[{"award-number":["20E01"]}]},{"name":"Scientific Research Foundation","award":["BSQD2020054"],"award-info":[{"award-number":["BSQD2020054"]}]},{"name":"Scientific Research Foundation","award":["BSQD2020055"],"award-info":[{"award-number":["BSQD2020055"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth\u2019s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 \u00d7 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10\u00d7 larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2\u00d7 faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications.<\/jats:p>","DOI":"10.3390\/rs14184514","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1914-9430","authenticated-orcid":false,"given":"Zhiqi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1037-1652","authenticated-orcid":false,"given":"Wen","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2266-5620","authenticated-orcid":false,"given":"Jinshan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangqi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Qu, Z., Liu, S., Li, D., Cao, J., and Xie, G. 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