{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:12:50Z","timestamp":1760242370491,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,18]],"date-time":"2017-05-18T00:00:00Z","timestamp":1495065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-12-MONU-0001"],"award-info":[{"award-number":["ANR-12-MONU-0001"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article is concerned with the use of unsupervised methods to process very high resolution satellite images with minimal or little human intervention. In a context where more and more complex and very high resolution satellite images are available, it has become increasingly difficult to propose learning sets for supervised algorithms to process such data and even more complicated to process them manually. Within this context, in this article we propose a fully unsupervised step by step method to process very high resolution images, making it possible to link clusters to the land cover classes of interest. For each step, we discuss the various challenges and state of the art algorithms to make the full process as efficient as possible. In particular, one of the main contributions of this article comes in the form of a multi-scale analysis clustering algorithm that we use during the processing of the image segments. Our proposed methods are tested on a very high resolution image (Pl\u00e9iades) of the urban area around the French city of Strasbourg and show relevant results at each step of the process.<\/jats:p>","DOI":"10.3390\/rs9050495","type":"journal-article","created":{"date-parts":[[2017,5,23]],"date-time":"2017-05-23T01:47:33Z","timestamp":1495504053000},"page":"495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0508-8550","authenticated-orcid":false,"given":"J\u00e9r\u00e9mie","family":"Sublime","sequence":"first","affiliation":[{"name":"LISITE Laboratory, RDI Team\u2013Institut Sup\u00e9rieur d\u2019\u00c9lectronique de Paris, 10 rue de Vanves, 92130 Issy Les Moulineaux, France"},{"name":"CNRS UMR 7030 LIPN\u2013Universit\u00e9 Paris 13, Sorbonne Paris Cit\u00e9, 99 av. J-B Cl\u00e9ment, 93430 Villetaneuse, France"}]},{"given":"Andr\u00e9s","family":"Troya-Galvis","sequence":"additional","affiliation":[{"name":"CNRS UMR 7357 ICube\u2013Universit\u00e9 de Strasbourg, 300 bd S\u00e9bastien Brant-CS 10413, F-67412 Illkirch CEDEX, France"}]},{"given":"Anne","family":"Puissant","sequence":"additional","affiliation":[{"name":"CNRS UMR 7362 LIVE\u2013Universit\u00e9 de Strasbourg, 3 rue de l\u2019Argonne, 67000 Strasbourg, France"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. 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