A novel multi-resolution segmentation algorithm for highresolution remote sensing imagery based on minimum spanning tree and minimum heterogeneity criterion

Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image segmentation based on multiple features, multi-resolution, and spatial context is one current research focus. Combining graph theory based optimization with the multi-scale image segmentation framework of the eCognition software, a multi-scale image segmentation method is proposed in this paper. In this method, a coherent enhancement anisotropic diffusion filtering approach and a minimum spanning tree segmentation algorithm are employed to initially segment the image. After that, the resulting segments are merged regarding minimum heterogeneity criteria, which are based on both the spectral characteristics and the shape parameters of segments. Two test images were used for visual and quantitative comparisons of the proposed method with the multi-scale segmentation method FNEA employed in the eCognition software. The results show that the proposed method is effective, and is more sensitive to subtle spectral differences than the FNEA.