Abstract
Remote sensing image segmentation is the basis of image pattern recognition. It is significant for the application and analysis of remote sensing images. Clustering analysis as a non-supervised learning method is widely used in the segmentation of remote sensing images. It has made good results in the segmentation of low-resolution and moderate-resolution remote sensing images. As the improvement of image resolution, however, they have problems in the segmentation of high-resolution remote sensing images. In this paper we propose an Agglomerative Hierarchical Clustering based High-Resolution Remote Sensing Image Segmentation Algorithm. The segmentation experiments show that the result of this algorithm is better than the K-Means’ and is close to the results of artificial extraction.