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

ArticleinPattern Recognition Letters 27(4):244-251 · March 2006with5 Reads
Impact Factor: 1.55 · 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.