Semi-automatic choice of scale-dependent features for satellite SAR image classification

Authors:
F. Dell'Acqua;P. Gamba;G. Trianni
Affiliations:
Dipartimento di Elettronica, Universití di Pavia, Via Ferrata, 1, 1-27100 Pavia, Italy;Dipartimento di Elettronica, Universití di Pavia, Via Ferrata, 1, 1-27100 Pavia, Italy;Dipartimento di Elettronica, Universití di Pavia, Via Ferrata, 1, 1-27100 Pavia, Italy
Venue:
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Year:
2006

Citing 1
Cited 3

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WSEAS Transactions on Computers

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Abstract

In this work we compare two different approaches to the use of multiple scales in the classification process of satellite SAR images. These are (I) the multi-scale co-occurrence texture analysis and (II) the semivariogram approach. Moreover, we propose a scheme for optimizing the co-occurrence window size and the semivariogram lag distances in terms of classification accuracy performance. To improve the results even further, we introduce a methodology to compute the co-occurrence features with a window consistent with the local scale, provided by the semivariogram analysis. Examples of satellite SAR image segmentation for urban area characterization are shown to validate the procedure.