<title>Wavelet texture analysis for remote sensing</title>

Abstract

In this paper we investigate the use of wavelet transforms to texture segmentation of Remotely Sensed images. The method adopted is multiresolution with maximum overlap. Various wavelet filters are considered (two different types of Daubechies, Battle-le Marie filters and Haar). To investigate the usefulness of these filters and the relevance of the various resolution levels, we introduce a novel probe: For the feature derived from a certain filter combination, we calculate the 2-point correlation function in the feature domain. This function allows us to judge whether this particular feature segregates the data into clusters or not. We also show that it gives an indication of the number of clusters present in the feature space. At the end we identify the useful features and perform image segmentation using all of them with the help of a C-means clustering technique. We conclude that the most useful results are obtained by using the Daubechies coiflet filter.