An Unsupervised Artificial Neural Network Method for Satellite Image Segmentation

Image segmentation is an essential step in image processing. The goal of segmentation is to simplify and/or tochange the representation of an image into a form easier to analyze. Many image segmentation methods are available but mostof these methods are not suitable for satellite images and they require a priori knowledge. In order to overcome theseobstacles, a new satellite image segmentation method is developed using an unsupervised artificial neural network methodcalled Kohonenʹs self-organizing map and a threshold technique. Self-organizing map is used to organize pixels according togrey level values of multiple bands into groups then a threshold technique is used to cluster the image into disjoint regions, this new method is called TSOM. Experiments performed on two different satellite images confirm the stability, homogeneity, and the efficiency (speed wise) of TSOM method with comparison to the iterative self-organizing data analysis method. Thestability and homogeneity of both methods are determined using a procedure selected from the functional model