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An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE)

文献类型：会议论文

作者Yu P.; Yu P.
出版日期2012
会议名称2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012
会议地点Nanjing, China
关键词In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA) genetic algorithm (GA) and artificial neural networks (ANNs). First some characteristics of the hyperspectral remotely sensed data such as high correlation high redundancy etc. are investigated. Based on the above analysis we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
收录类别EI
源URL[http://ir.ciomp.ac.cn/handle/181722/34018]  
专题长春光学精密机械与物理研究所_中科院长春光机所知识产出_会议论文
推荐引用方式
GB/T 7714		 Yu P.,Yu P.. An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE)[C]. 见:2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012. Nanjing, China.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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