Future Generation Computer Systems
Image Thresholding Using Ant Colony Optimization
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Texture image recognizing method based on ant colony algorithm
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Object segmentation using ant colony optimization algorithm and fuzzy entropy
Pattern Recognition Letters
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Segmentation of remote sensing image is not only a hot topic but a difficult technological field in remote sensing image processing as well. Recently, Ant Colony Optimization (ACO) algorithm has been introduced into image segmentation. But seldom study has been done in segmentation of multispectral remote sensing images based on Ant Colony Optimization Algorithm. In this paper, ACO algorithm is used in segmentation of multispectral remote sensing images. Three vectors of multispectral remote sensing images at each pixel site are extracted as eigenvectors, such as multi-spectrum gray values at one pixel site, mean gray values of neighborhood pixels in each band, and multi-spectrum gradient values at one pixel site. They reflect both value features and spatial features of remote sensing images. The combination of these three eigenvectors is used as the fuzzy cluster features. Furthermore, ACO Algorithm is used to optimize fuzzy clustering process. This method not only improves the segmentation result of multispectral remote sensing images, but also controls calculation amount effectively. Experiment and comparison results show that fuzzy clustering algorithm optimized by ACO is a preferable mothod for segmentation of multispectral remote sensing images.