Texture classification in aerial photographs and satellite data

Texture features have proved to be an important tool in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. Many methods were suggested to achieve good discrimination between different textural regions. We propose a non-supervised classification method. The method combines a multi-resolution based texture feature with features based on first and second order statistics. These features are calculated for each pixel in the image and its neighbours. A clustering algorithm, based on the generalized Lloyd algorithm, is applied and finally an iterative region merging process, based on the phagocytes heuristic, is used to improve the classification of the borders of the regions and to reduce local clustering errors. Examples of the method and the features effectiveness are presented using SPOT satellite images.