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• W1630146544 abstract "ABSTRACT: The classification task with only one single monoband radar image, for cost and image availability reasons, is a hard task due to nature of these images. In this paper, we present an attempt for the classification of an ERS-1 radar image for the need of thematic cartography. The drawback of monoband Radar image is surmounted by the integration of the spatial dimension of the image, by using a redundant wavelet transformation. A supervised classification process, which combines both radiometric and textural information’s, in a multi-resolution scheme is applied. The results obtained over a single ERS-1 image, which cover a relatively complex area, are satisfactory. * A. SAFIA: safia.abdelmounaime@gmail.com. Envisat & ERS Symposium. Volume572, Start Page167, ID: 51225917, Austria (2005) 1. INTRODUCTION In a digital image, texture is as an organized spatial phenomenon of pixel values. A texture object is a particular or specific organisation of pixels. Previous methods of analysis for accomplishing texture classification may be roughly divided into three categories: statistical, structural and spectral. These methods have been successful in many fields but they share one common weakness. That is, focus on the coupling between image pixels on a single scale. Among methods allowing multiresolution analysis, the wavelet transform plays an important part in texture analysis. It’s well known that SAR (synthetic aperture radar) images have a high textural content. Image classification based on texture features is still a very important aspect in SAR data analysis and segmentation. Various SAR image classifications using textural-based method can be found in literature. In the most cases, the SAR data are used in a multi-date scheme. None of the consulted documentations have been taken into account the classification of a single SAR image, which can be justified by cost or data availability. This paper is organised as follow. Section 2 presents the area of study and the data used. Sections 3 and 4 present the basic concept of the wavelet transform and its adaptation to the texture analysis task. Sections 5 and 6 describe texture features extraction and classification. Experimental results, discussion and conclusion are given in section 7 and 8." @default.
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• W1630146544 date "2005-04-01" @default.
• W1630146544 modified "2023-09-23" @default.
• W1630146544 title "Performance Evaluation of a Combined Multi-Resolution Textural Classification of a Single ERS-1 Image" @default.
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