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浏览/检索结果: 共1条，第1-1条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China Mao W.; Yu P.; Guo B.; Xu Y.; Chen H. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 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.