Vers une approche collaborative segmentation-classification pour l'analyse d'images de télédétection

National audienceL'interprétation automatique d'images de télédétection à très haute résolution spatiale est une tâche complexe. Les approches d'analyse d'images orientée objets sont couramment utilisées afin de résoudre ce problème ; mais leurs résultats dépendent fortement sur la segmentation d'images. Or, il n'existe pas de méthode de segmentation universelle permettant d'isoler correctement les différents objets à extraire. Nous proposons ici un cadre formel de collaboration entre un segmenteur et un classeur pour améliorer conjointement la segmentation et la classification pour une classe thématique donnée

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

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

Remote sensing image analysis by aggregation of segmentation-classification collaborative agents

International audienc

Interactions segmentation-classification dans un cadre multi-paradigme pour l’analyse d’images de télédétection

International audienc

Collaborative segmentation and classification for remote sensing image analysis

International audienc

Sur l’évaluation et l’élaboration d’un jeu de données de référence de bonne qualité en télédétection

National audienc

Unsupervised quantification of under- and over-segmentation for object-based remote sensing image analysis

International audienceObject Based Image Analysis (OBIA) has been widely adopted as a common paradigm to deal with very high resolution remote sensing images. Nevertheless, OBIA methods strongly depend on the results of image segmentation. Many segmentation quality metrics have been proposed. Supervised metrics give accurate quality estimation but require a ground-truth segmentation as reference. Unsupervised metrics only make use of intrinsic image and segment properties; yet most of them strongly depend on the application and do not deal well with the variability of objects in remote sensing images. Furthermore, the few metrics developed in a remote sensing context mainly focus on global evaluation. In this article we propose a novel unsupervised metric which evaluates local quality (per segment) by analysing segment neighbourhood, thus quantifying under-and over-segmentation given a certain homogeneity criterion. Additionally, we propose two variants of this metric, for estimating global quality of remote sensing image segmentation by the aggregation of local quality scores. Finally, we analyse the behaviour of the proposed metrics and validate their applicability for finding segmentation results having good trade-off between both kinds of errors