Share Email Print

Proceedings Paper

Integration of scale-space filtering and neural techniques for high-resolution remote sensing image classification
Author(s): Elisabetta Binaghi; Ignazio Gallo; Monica Pepe
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in many remote sensing image classification studies. This work presents a supervised adaptive object recognition model which integrates scale-space filtering techniques for feature extraction within a neural classification procedure based on multilayer perceptron (MLP). The salient aspect of the model is the integration within the back-propagation learning task of the search of the most adequate filter parameters. The experimental evaluation of the method has been conducted coping with object recognition in high-resolution remote sensing imagery. To investigate whether the strategy can be considered an alternative to conventional procedures the results were compared with those obtained by a well known contextual classification scheme.

Paper Details

Date Published: 13 March 2003
PDF: 8 pages
Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); doi: 10.1117/12.463142
Show Author Affiliations
Elisabetta Binaghi, Univ. degli Studi dell'Insurbria (Italy)
Ignazio Gallo, CNR-ITIM (Italy)
Monica Pepe, CNR-ITIM (Italy)

Published in SPIE Proceedings Vol. 4885:
Image and Signal Processing for Remote Sensing VIII
Sebastiano B. Serpico, Editor(s)

© SPIE. Terms of Use
Back to Top