Segmentation of High Resolution Satellite Image Using S-Transform

The resolution of remote sensing images increases every day, raising the level of detail and the heterogeneity of the scenes. Most of the existing geographic information systems classification tools (Stock well Transform) have used the same methods for years. With these new high resolution images basic classification methods do not provide satisfactory results. A region-based classification method segmentation is based on and a classification. In this paper, we have proposed an approach for the segmentation of very high resolution (VHR) satellite images using S-Transforms. Satellite images have many applications in meteorology, agriculture, geology, forestry, landscape, biodiversity conservation, regional planning, education, intelligence and warfare. The segmentation uses an S-Transform to divide the image into several homogenous regions. Then follows the region-based classification performed either with the method MCL (Maximum Likelihood classifier). The method was validated and a comparison between pixel-based and region-based classification was performed. This method provides better results comparing to the existing remote sensing classification tools, even if some work should be done to prove its robustness. We also proved that the prior segmentation significantly improves the results of classification, both from the quantitative and qualitative points of view.