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
Semi-automatic choice of scale-dependent features for satellite SAR image classification
GAMBA, PAOLO ETTORE;DELL'ACQUA, FABIO;TRIANNI, GIOVANNA
2006-01-01
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 procedureI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.