A Clustering Technique for Remote Sensing Images Using Combination of Watershed Algorithm and Gustafson-Kessel Clustering

In clustering of remote sensing images by using conventional algorithms, not detects the boundaries of image properly. In this paper, an image clustering algorithm based on watershed algorithm and Gustafson-Kessel fuzzy clustering has been proposed. Initially, the watershed algorithm is used for segmentation of the image that is obtained of summing image derivative with the original image. Then, the average of pixels spectrum in each segment is selected as a representative of that area and using combination of the neighboring pixels data of each area with neighbor areas and Gustafson-kessel clustering, the average spectrum of different areas is clustered. Then to improve the clustering results, a new partition matrix is calculated for each area of the image according to the characteristics of neighbor areas. Because the remote sensing images are including a high noise level, proposed algorithm have a greater ability to opposition with noise than watershed algorithm and appears the image edges better. The results of proposed algorithm on a sample of remote sensing image show practicality and efficiency of the proposed algorithm.