Advances in texture-based segmentation of high resolution remote sensing imagery

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to high-resolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. Although for most test images TFR has proven able to recognize major semantic areas, some failures have also been observed due to the presence of large background regions that span the whole image and prevent the formation of distinct local textures. In this paper we introduce a new step in the TFR processing flow which detects background regions and divides them in multiple homogeneous fragments based on their geometric level properties. To this end, connected regions are first reduced to atomic components through a watershed-like transform, and then clustered again based on the features of the associated region-adjacency graph. Early experimental results prove the effectiveness of the new processing step, and its beneficial effect on the whole algorithm.