Study on Remote Sensing Image Segmentation Based on ACA–FCM

Abstract Image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. The traditional segmentation methods have obtained good results, but there are defects such as noise-sensitive, over-smoothing and loss of image information. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. ACA–FCM can greatly enhance the speed of image segmentation, while reducing the noise on the image. The image segmentation based on ACA–FCM is carried out and compared with traditional methods. Experimental results show that ACA–FCM can quickly and accurately segment target and it is an effective method of image segmentation.