文献类型：会议论文

英文题名：Quantitative retrieval of sparse vegetation coverage by BP neural network based on hyperspectral data

作者：Li, Xiaosong[1] Gao, Zhihai[2]

第一作者：Li, Xiaosong

通信作者：Li, X.

机构：[1] Institute of Remote Sensing Applications, Chinese Academy of Science, Beijing, China; [2] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China

会议论文集：Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

会议日期：16 October 2010 through 18 October 2010

会议地点：Yantai

语种：英文

外文关键词：Backpropagation - Complex networks - Photomapping - Signal processing - Vegetation

年份：2010

摘要：With parallel processing, nonlinear mapping, adaptive learning and fault-tolerant capacities, neural net has always been the ideal tool for establishing the complex relationship between complex spectral information and target parameters. We constructed a two-layer BP neural net model with three input nodes, two hidden layer nodes and one output node. Based on which, sparse vegetation coverage in Minqin oasis-desert transitional zone, Gansu province, was estimated through selecting independent principal component as input. The results show that the accuracy of retrieval based on BP neural net was high, validation RMSE was only 3.2806, about 16% in the average. Therefore, BP neural network model is an effective means to retrieve sparse vegetation coverage accurately n arid regions, based on hyperspectral data. ?2010 IEEE.