VERY HIGH SPATIAL RESOLUTION IMAGE SEGMENTATION BASED ON THE MULTIFRACTAL ANALYSIS

The availability of very high spatial resolution images in remote sensing brings the texture segmentation of images to a higher level of complexity. Such images have so many details that the classical segmentation algorithms fail to achieve good results. In the case of IKONOS images of forest areas, a texture can be so different within a same class that it becomes very difficult even for a human to segment or interpret those images. The study of the high frequency content of the data seems to be a good way to study those images. We work on a new method which uses the singularity information to achieve the segmentation. It is based on the computation of the Hölder regularity exponent at each point in the image. From this parameter we can compute the local Legendre or the large deviation multifractal spectrum which gives information about the geometric distribution of the singularities in the image. So we use global and local descriptors of the regularity of the signal as input parameters to a k-means algorithm. The whole algorithm is described and applied to IKONOS images as well as to an image made of brodatz textures. The segmentation results are compared to those obtained from the laws filters and the co-occurrence parameters techniques. The proposed method gives better results and is even able to segment the image in tree density classes