{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:54:42Z","timestamp":1788303282688,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scene understanding of satellite and aerial images is a pivotal task in various remote sensing (RS) practices, such as land cover and urban development monitoring. In recent years, neural networks have become a de-facto standard in many of these applications. However, semantic segmentation still remains a challenging task. With respect to other computer vision (CV) areas, in RS large labeled datasets are not very often available, due to their large cost and to the required manpower. On the other hand, self-supervised learning (SSL) is earning more and more interest in CV, reaching state-of-the-art in several tasks. In spite of this, most SSL models, pretrained on huge datasets like ImageNet, do not perform particularly well on RS data. For this reason, we propose a combination of a SSL algorithm (particularly, Online Bag of Words) and a semantic segmentation algorithm, shaped for aerial images (namely, Multistage Attention ResU-Net), to show new encouraging results (i.e., 81.76% mIoU with ResNet-18 backbone) on the ISPRS Vaihingen dataset.<\/jats:p>","DOI":"10.3390\/rs13163275","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T04:13:54Z","timestamp":1629346434000},"page":"3275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1442-7155","authenticated-orcid":false,"given":"Valerio","family":"Marsocci","sequence":"first","affiliation":[{"name":"Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0881-8344","authenticated-orcid":false,"given":"Simone","family":"Scardapane","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos","family":"Komodakis","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Crete, 70013 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. 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