{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:13:20Z","timestamp":1740154400755,"version":"3.37.3"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,22]],"date-time":"2018-08-22T00:00:00Z","timestamp":1534896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0502600"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601014"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.<\/jats:p>","DOI":"10.3390\/rs10091339","type":"journal-article","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T07:00:46Z","timestamp":1535007646000},"page":"1339","source":"Crossref","is-referenced-by-count":76,"title":["ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Shuo","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Wenrui","family":"Ding","sequence":"additional","affiliation":[{"name":"Unmanned Systems Research Institute, Beihang University, Beijing 100191, China"}]},{"given":"Chunhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Unmanned Systems Research Institute, Beihang University, Beijing 100191, China"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8713-3153","authenticated-orcid":false,"given":"Yufeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Hongguang","family":"Li","sequence":"additional","affiliation":[{"name":"Unmanned Systems Research Institute, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/MSP.2011.940409","article-title":"Parallel Hyperspectral Image and Signal Processing [Applications Corner]","volume":"28","author":"Plaza","year":"2011","journal-title":"IEEE Signal Process. 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