4 March 2013 Monitoring land coverage change in mining area by remote sensing image classification
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Proceedings Volume 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering; 87610C (2013) https://doi.org/10.1117/12.2019647
Event: Third International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), 2013, Sanya, China
Abstract
Based on remote sensing images, the panoramic views of land coverage distribution across a large geographic area can be accessed conveniently. In order to improve the accuracy of monitoring land use changes, the Chaos Genetic Algorithm was proposed. Chaos Immune Algorithm has capability of self-organizing, self-learning, self-recognition and self-memory, hence through the input samples the global optimization clustering center was found. And then the clustering center was employed to classify the view picture of remote sensing image. In this process, the ergodic property of chaos phenomenon was used to optimize the initial antibody population, so it could accelerate the convergence of Immune Algorithm. Through the clone selection operator, mutation operator and recruited antibody, local optimums were avoid. Chaos Immune Algorithm was applied to classify land use in Huainan –based on TM image. Based on confusion matrix, the classification of the Parallelepiped and Maximum likelihood methods were contrasted with Chaos Immune Algorithm. It is demonstrated that Chaos Immune Algorithm is superior to the two traditional algorithms, and its overall accuracy and Kappa coefficient reach 88.26% and 0.853respectively.
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Wei Wang, Wei Wang, Li Zhao, Li Zhao, Yanbin Wu, Yanbin Wu, } "Monitoring land coverage change in mining area by remote sensing image classification", Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 87610C (4 March 2013); doi: 10.1117/12.2019647; https://doi.org/10.1117/12.2019647
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