{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:38:19Z","timestamp":1762868299489,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T00:00:00Z","timestamp":1601251200000},"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","doi-asserted-by":"publisher","award":["41701508","61725105"],"award-info":[{"award-number":["41701508","61725105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road extraction from optical remote sensing images has drawn much attention in recent decades and has a wide range of applications. Most of the previous studies rarely take into account the unique topological characteristics of the road. It is the most apparent feature of linear structure that describes the variety of connection relationships of the road. However, designing a particular topological feature extraction network usually results in a model that is too heavy and impractical. To address the problems mentioned above, in this paper, we propose a lightweight topological space network for road extraction based on knowledge distillation (TSKD-Road). Specifically, (1) narrow and short roads easily influence topological features extracted directly in optical remote sensing images. Therefore, we propose a denser teacher network for extracting road structures; (2) to enhance the weight of topological features, we propose a topological space loss calculation model with multiple widths and depths; (3) based on the above innovations, a topological space knowledge distillation framework is proposed, which aims to transfer different kinds of knowledge acquired in a heavy net to a lightweight net, while significantly improving the lightweight net\u2019s accuracy. Experiments were conducted on two publicly available benchmark datasets, which show the obvious superiority and effectiveness of our network.<\/jats:p>","DOI":"10.3390\/rs12193175","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"3175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Kai","family":"Geng","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Yan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Diao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TVT.2013.2281199","article-title":"A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios","volume":"63","author":"Li","year":"2013","journal-title":"IEEE Trans. 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