Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example

Xin Mei; Qian Wang; Quanfang Wang; Wenfang Lin

doi:10.1117/12.833156

30 October 2009 Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example

Xin Mei, Qian Wang, Quanfang Wang, Wenfang Lin

Author Affiliations +

Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74942F (2009) https://doi.org/10.1117/12.833156
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

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. 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. Reasonably fast image segmentation algorithm is the base of image processing; traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work.

Citation Download Citation

Xin Mei, Qian Wang, Quanfang Wang, and Wenfang Lin "Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74942F (30 October 2009); https://doi.org/10.1117/12.833156

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