A Hybrid Method for Preprocessing and Classification of SPOT Images

Shan Yu 1 Konorad Weigle 1
1 PASTIS - Scene Analysis and Symbolic Image Processing
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this paper, we present a hybrid method for preprocessing and classification of satellite images. The preprocessing consists of computing texture measures of the images and initialising the probabilities of pixels belonging to different land-cover classes. The objective of the preprocessing is twofold: increasing discrimination power and removing irrelevant characteristics. The classification process consists of assigning a class to each pixel, with a special interest in detecting urban areas as completely as possible with the aid of a priori knowledge. This interest stems from the possible requirement of detecting urban areas on satellite images (even small villages in the countryside) while ignoring some classes (such as parks) in cities. We shall show how this requirement is translated into constraints imposed in our classification process. Experimental results are illustrated through a SPOT image containing a coastal town.
Type de document :
Communication dans un congrès
Ioannis Kanellopoulos; Graeme G. Wilkinson; Fabio Roli; James Austin. European Workshop on Connectionist Methods for Preprocessing and Analysis of Remote Sensing Data, Jul 1996, York, United Kingdom. Springer, Neurocomputation in Remote Sensing Data Analysis, pp.134-141, 〈10.1007/978-3-642-59041-2_15〉
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https://hal.inria.fr/hal-01087885
Contributeur : Shan Yu <>
Soumis le : jeudi 27 novembre 2014 - 05:11:43
Dernière modification le : samedi 27 janvier 2018 - 01:31:31

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Shan Yu, Konorad Weigle. A Hybrid Method for Preprocessing and Classification of SPOT Images. Ioannis Kanellopoulos; Graeme G. Wilkinson; Fabio Roli; James Austin. European Workshop on Connectionist Methods for Preprocessing and Analysis of Remote Sensing Data, Jul 1996, York, United Kingdom. Springer, Neurocomputation in Remote Sensing Data Analysis, pp.134-141, 〈10.1007/978-3-642-59041-2_15〉. 〈hal-01087885〉

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