Segmentation for high spatial resolution remote sensing images by combining quadtree with minimum spanning tree

This paper presents a high spatial resolution remote sensing image segmentation method by combining quadtree with minimum spanning tree. Firstly, the improved quadtree segmentation algorithm is used to divide the image iteratively into many over-segmented objects, which greatly facilitates the selection of initial segmentation parameters. Then the improved Morton coding is used to construct the spatial index of the generated over-segmented object and form the region adjacency relation. Combine spectral and texture features, the similarity between adjacent regions is calculated and the region merging criterion is constructed. Based on the idea of minimum spanning tree, the over-segmented objects are merged to generate multiple minimum spanning trees. During that process, the number of minimum spanning trees can be controlled to obtain ideal segmentation results. Compared with two other segmentation algorithms, the method proposed in this paper is more convenient to select segmentation parameters and has certain improvement in segmentation accuracy and object integrity of segmentation results.