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Remote Sens. 2017, 9(5), 495;

Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques

LISITE Laboratory, RDI Team–Institut Supérieur d’Électronique de Paris, 10 rue de Vanves, 92130 Issy Les Moulineaux, France
CNRS UMR 7030 LIPN–Université Paris 13, Sorbonne Paris Cité, 99 av. J-B Clément, 93430 Villetaneuse, France
CNRS UMR 7357 ICube–Université de Strasbourg, 300 bd Sébastien Brant-CS 10413, F-67412 Illkirch CEDEX, France
CNRS UMR 7362 LIVE–Université de Strasbourg, 3 rue de l’Argonne, 67000 Strasbourg, France
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Sangram Ganguly and Prasad S. Thenkabail
Received: 15 March 2017 / Revised: 25 April 2017 / Accepted: 12 May 2017 / Published: 18 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [10421 KB, uploaded 18 May 2017]


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 (Pléiades) of the urban area around the French city of Strasbourg and show relevant results at each step of the process. View Full-Text
Keywords: very high resolution images; segmentation; multi-scale clustering very high resolution images; segmentation; multi-scale clustering

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Sublime, J.; Troya-Galvis, A.; Puissant, A. Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques. Remote Sens. 2017, 9, 495.

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