An object-level approach improved by quadtree to dynamic monitoring of mining area expansion

Abstract

An object-level approach improved by quadtree to dynamic monitoring of mining area expansion is proposed. In order to improve the efficiency and quality of objects acquired from high spatial resolution remote sensing image, multi-scale segmentation combined with quadtree segmentation is used to obtain objects of multitemporal remote sensing images; Then object-oriented image analysis method which takes into account the spatial relationship between ground objects is used in multitemporal remote sensing images to extract mining information respectively; Finally, overlay is use in mining areas extraction respectively, and Inter-erase operation is used to obtain result of mining expansion. Experiments are carried out in remote sensing images from a certain phosphate area of Anning, and the results prove that method was proposed in this paper is feasible and effective. © 2013 Springer-Verlag.