15 November 2007 Application of ant colony optimization (ACO) algorithm to remote sensing image classification
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Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881A (2007) https://doi.org/10.1117/12.749344
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Ant Colony Optimization (ACO) algorithm takes inspiration from the coordinated behavior of ant swarms, which has been applied in many study fields as a novel evolutionary technology to solve optimization problems. But it has rarely been used to process remote sensing data. Using the ACO algorithm to remote sensing image classification does not assume an underlying statistical distribution for the pixel data, the contextual information can be taken into account, and it has strong robustness. In this paper, taking Landsat TM data as an example, the process of ACO method in remote sensing data classification is introduced in detail, and has achieved a good result. The study results suggest that ACO become a new effective method for remote sensing data processing.
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Qin Dai, Qin Dai, Jianbo Liu, Jianbo Liu, } "Application of ant colony optimization (ACO) algorithm to remote sensing image classification", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881A (15 November 2007); doi: 10.1117/12.749344; https://doi.org/10.1117/12.749344
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