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

Dipartimento di Elettronica, Università di Pavia, Via Ferrata, 1, 1-27100 Pavia, Italy
Pattern Recognition Letters (Impact Factor: 1.06). 03/2006; 27:244-251. DOI: 10.1016/j.patrec.2005.08.005
Source: DBLP

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.

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