| As one of the key techniques in our understanding,analyzing and application of image information, remote sensing image processing include the radiation correction,geometric correction, image mosaic, projection transform, feature extraction, classification, segmentation and various special processing method of remote sensing image. Segmentation plays a significant role in the analysis and feature extraction of remote sensing image. Ant colony algorithm is a new bionic evolution algorithm with the excellent characteristics of adaptive, self-organizing, collaborative and highly parallel. Therefore, in this paper, ant colony algorithm is applied to the study of remote sensing image segmentation, trying to explore an effective method of the segmentation of high resolution remote sensing image. Through summarizing the research status of ant colony algorithm and related problem in the recent study, this paper gives a detail introduction of the basic principles of ant colony algorithm, image segmentation model, the existing improve method and the application of segmentation in remote sensing image. Particularly described the improvement strategy of the improved ant colony algorithm, it uses an experiment of image segmentation choosing Quick Bird Image of2010,March in Xiangyang City, Hubei province with the improved ant colony algorithm, then compares the results between ant colony segmentation algorithm and common segmentation, which shows that the improved strategies and application of algorithms of this paper are adaptable and available.In general, the ant colony optimization algorithm improvement strategy propose in this paper is a kind of effective improvement, which successfully apply to the remote sensing image segmentation research. Through the comparison of image segmentation algorithms with other methods, we can see that the improved ant colony algorithm is feasible and effective. There is a good application prospect in the field of remote sensing image segmentation. |
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