Using a wavelet-based fractal feature to improve texture discrimination on SAR images

Clustering is commonly used in remote sensing image segmentation. Among the clustering techniques, pyramid-based methods generally provide better performance in discriminating among different cover classes if compared to global algorithms. When applied to single polarization synthetic aperture radar (SAR) data, though, such algorithms suffer from misinterpretation problems due to the mono-band nature of the images produced by these sensors. In this case an important feature to improve the segmentation is texture. This paper describes a wavelet-based fuzzy clustering algorithm which receives as input both the remotely sensed image and a texture image based on a fractal model, derived from the wavelet representation itself. The algorithm has been tested on X-SAR images, and the results demonstrate its potential usefulness.