Abstract
A heuristic genetic algorithm (GA)-based support vector classifier (SVC) for recognition of remote sensing images is presented in this paper. The model parameters of SVC are automatic selected by a heuristic GA to obtain the better performance with high efficiency. Compared with the leave-one-out (loo) method and the trial and error method, this GA-based model parameters selection is simpler and easier to implement. Furthermore, the generalization of the obtained SVC is much improved. Comparative tests conducted on a 2-value remote sensing images demonstrate the better result of the proposed classifier.
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Zheng, C., Zheng, G., Jiao, L. (2004). Heuristic Genetic Algorithm-Based Support Vector Classifier for Recognition of Remote Sensing Images. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_103
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DOI: https://doi.org/10.1007/978-3-540-28647-9_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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