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Proceedings Paper

A scale-driven classification technique for very high geometrical resolution images
Author(s): Lorenzo Bruzzone; Lorenzo Carlin
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Paper Abstract

In this paper, we propose a novel scale-driven technique for classification of very high geometrical resolution images. This technique is aimed at obtaining accurate and reliable classification maps by properly preserving the geometrical details present in the images and at the same time by accurately representing the homogeneous areas. The proposed method, on the basis of a multi-scale decomposition of the image under investigation, adaptively selects the proper number of scales to be used in the classification of each single pixel. It is composed of three main steps: i) multiscale/multiresolution decomposition of the considered high resolution image; ii) adaptive selection of the set of best representative scales (levels) of each pixel; iii) classification on the basis of the selected scales. In greater detail, in the first step a multiscale/multiresolution decomposition based on the Gaussian Pyramid analysis is applied to the image under investigation. To correctly classify homogeneous areas while preserving the geometrical information of the scene (details), in the second step we propose to select the set of scales that better represent each specific pixel analyzed. This task is accomplished by a proper adaptive strategy based on the analysis of the neighbor of each pixel at different scales. To obtain the final classification map, in the third step, the selected scales are used as input to a classification architecture composed of different SVM classifiers. Experimental results carried out on a very high geometrical resolution Quickbird image confirm the effectiveness of the proposed technique.

Paper Details

Date Published: 18 October 2005
PDF: 12 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 598203 (18 October 2005); doi: 10.1117/12.628847
Show Author Affiliations
Lorenzo Bruzzone, Univ. of Trento (Italy)
Lorenzo Carlin, Univ. of Trento (Italy)

Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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