Morphological texture features for unsupervised and supervised segmentations of natural landscapes

The goal of this paper is to segment high-resolution images of natural landscapes into different cover types. With this aim, morphological texture features (descriptors of random sets obtained by morphological transformations) are used in order to avoid the limitations of spectral features. First, a supervised segmentation (the textures to detect having been previously determined) is presented. The classes correspond to different degrees of tree densities. Second, a methodology for an unsupervised texture segmentation (no a priori information about the textures is supplied) is proposed. The number of classes is automatically determined. The proposed procedures have been tested on several images, providing promising results.