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Vol. 157, Issue 10, October 2013, pp. 6-13




Object-Oriented Classification of Hyperspectral Remote Sensing Images Based on Genetic Algorithm and Support Vector Machine
Hongmin GAO, Lizhong XU, Chenming LI, Xin WANG, Minggang XU

College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China
E-mail: banjamin2006@163.com, lcm@hhu.edu.cn


Received: 11 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems


Abstract: This paper proposes a method of reducing dimensions based on genetic algorithm and object-oriented classification based on support vector machine (SVM). The basic idea is subspace decomposition of hyperspectral images at first, then selecting suitable bands in each subspace by using genetic algorithm and putting all selected bands of each subspace together. Furthermore, the hyperspectral image is segmented into a series of objects and then the spectral features and spatial features of objects in the selected bands are extracted. Finally, SVM classification is used according to features of the objects. The algorithm proposed is more effective and superior in dimension reduction and classification of hyperspectral image.


Keywords: Hyperspectral image classification, Genetic algorithm, Support vector machine, Band selection


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