A Multi-scale Segmentation Method for Remotely Sensed Images Based on Granulometry

This paper proposed a multi-scale segmentation method for remote sensing image based on mathematical morphology. In mathematical morphological operations, opening transform can extract lighter connected components and closing transform can extract darker ones with size smaller than a given structure element in a gray image. A connected component, as an object, may have a high response to a given structure element size and a lower response to others. In this paper, granulometry and anti-granulometry were used for detecting the most sensitive element structures of objects from a range of structure elements with different sizes. Granulometry, an image sequence, was obtained by a series of opening transforms to the original image by using a family of structure elements with an integral index set. Anti-granuometry was generated by closing transforms. The resulting image sequences of granulometry and unti-granuometry were then operated by a series of derivatives , and the maximum value at each pixel corresponds to the index of the most sensitive structure element. The index was taken as the morphological characteristic of the corresponding pixel. The proposed segmentation method in this paper is based on the assumption that pixels with similar morphological features belong to the same connected component. This method avoids the problems of over-segmentation and boundary pixels occurred in the classical method of morphological segmentation.