{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:29:18Z","timestamp":1773772158960,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the China Central Universities of USTB","award":["FRF-BR-17-004A"],"award-info":[{"award-number":["FRF-BR-17-004A"]}]},{"name":"the Fundamental Research Funds for the China Central Universities of USTB","award":["FRF-GF-17-B49"],"award-info":[{"award-number":["FRF-GF-17-B49"]}]},{"name":"the Open Project Program of the National Laboratory of Pattern Recognition","award":["NLPR, 201800027"],"award-info":[{"award-number":["NLPR, 201800027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.<\/jats:p>","DOI":"10.3390\/s18103232","type":"journal-article","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T10:39:58Z","timestamp":1537958398000},"page":"3232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images"],"prefix":"10.3390","volume":"18","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}]},{"given":"Qirui","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}]},{"given":"Jiahui","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3125-3847","authenticated-orcid":false,"given":"Meng","family":"Ding","sequence":"additional","affiliation":[{"name":"Bayer HealthCare, Pittsburgh, PA 15238, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2288-7901","authenticated-orcid":false,"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11760-015-0804-2","article-title":"Land-use scene classification using multi-scale completed local binary patterns","volume":"10","author":"Chen","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"042620","DOI":"10.1117\/1.JRS.11.042620","article-title":"Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds","volume":"11","author":"Maltezos","year":"2017","journal-title":"J. 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