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	A Star-Identification Algorithm Based on Global Multi-Triangle Voting 期刊论文 OAI收割 APPLIED SCIENCES-BASEL, 2022, 卷号: 12, 期号: 19 作者: Yuan, Xiaobin; Zhu, Jingping; Zhu, Kaijian; Li, Xiaobin \| 收藏 \| 浏览/下载：48/0 \| 提交时间：2022/10/25 star identification feature unit voting two-dimension lookup table principal component analysis (PCA) largest cluster method
	An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者: Yu P.; Yu P. 收藏 \| 浏览/下载：34/0 \| 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.
	Chaotic analysis on monthly precipitation on hills region in middle SiChuan, China 会议论文 OAI收割 Men B. H. 收藏 \| 浏览/下载：18/0 \| 提交时间：2012/06/30 correlation dimension hills region in middle of SiChuan principal component analysis (PCA) method kolmogorov entropy time-series deterministic chaos strange attractors river flow rainfall nonlinearity redundancies storm