A Segmentation Algorithm for Remote Sensing Imaging Based on Edge and Heterogeneity of Objects

Abstract

Image segmentation is the fundamental technology used in object-oriented remote sensing image analysis. To improve on the approach of object-oriented image analysis for the extraction of ground objects of interest, a segmentation algorithm for remote sensing images based on edge and heterogeneity of objects has been proposed in this paper. Canny algorithm combined with a mathematical morphology operator is used first to detect the edge information of a remote sensing image. Then, based on edge and heterogeneity of images, homogenous regions are obtained by Fractal Net Evolution approach. Finally, the edge information is used as a constrain in region merging, the final segmentation results are acquired. In this paper, QuickBird data from a certain area of Kunming have been tested and compared with a multi-resolution segmentation algorithm in the eCognition platform. The experiment results show that the method can segment the remote sensing images effectively and precisely.