Texture Features for Segmentation of Satellite Images

To be able to find different textures in an image, a simple strategy is to perform texture measurements on a moving window and assign scalar features to each of the image pixels corresponding to window centers. This operation is similar to filtering. It transforms an image into a feature image. Three novel texture features for image segmentation based on gray level texture are presented and compared. They use 3x3, 5x5 and 7x7 neighborhood masks and provide a quantitative measure of image texture using only diagonal pixels (first and second features) or all pixels of the masks (third feature). The average intensity value in the feature masks is computed. The proposed approach to compute texture features is theoretically and computationally simple: a texture feature value is a difference of the gray level value of the central pixel and the average intensity value in the feature masks. The texture feature images are used in experiments for satellite image texture segmentation. In this study, an unsupervised texture-based image segmentation algorithm is discussed. The most common image segmentation methods have been applied to the feature images: fuzzy c-means, gray level quantization, histogram thresholding, median cut and principal components transformation/median cut. Results from texture segmentation are presented and analyzed.