An Unsupervised Neuro-Morphological Approach for Satellite Image Segmentation

In this paper, we present a novel unsupervised segmentation approach applied to satellite images. This neuro-morphological approach is based on both Kohonen maps and mathematical morphology, Indeed, to segment a satellite image we extract textural and color information from each pixel of the image, the primitives extracted are used in the learning stage of our Kohonen Self-Organizing Map (KSOM), followed by a two-dimensional representation of this map by an underlying probability density function (PDF) ; thus, a segmentation of the map is applied to this representation by the use of a proposed watershed method (LPE). This will help us to segment our image by assigning each pixel of the image to a modal region extracted from the KSOM. The extraction step of our approach is accelerated by a proposed parallel implementation using the CUDA environment on NVIDIA GPUs. In order to validate our segmentation approach, all the results obtained are compared with the K-means segmentation method.