国土资源遥感Issue(2)：7-11,5.DOI:10.6046/gtzyyg.2012.02.02
基于高光谱数据和RBF神经网络方法的草地叶面积指数反演
The Application of Hyper -spectral Data and RBF Neural Network Method to Retrieval of Leaf Area Index of Grassland
摘要
Abstract
In accordance with the 120 sites of grassland canopy spectral reflectance and the leaf area index (LAI) data collected by Chinese Academy of Agricultural Science, the method of Radial Basis Function (RBF) neural network was developed for the prediction of LAI after the compression of spectral reflectance using principal component analysis ( PCA). The PCA results show that the cumulative reliability of the first 9 PCs is up to 99.782% , covering the majority of original spectral information. The 120 sites of LAI and 9 PC samples were classified randomly for training daiaset (90 sites) and predicting dataset (30 sites) ,and were used to establish the neural network and predict the LAI, respectively. The results show that the accuracy rate of training data is up to 100% (RMSE = 0.009 6, R2 = 0.999). The root mean square error ( RMSE) and correlation coefficient (R2) for the prediction dataset are 0.839 and 0.218 6 respectivdg, thus achieving more preferable results and improved the accuracy (RMSE =0.416 5,R2 =0.570)of the traditional multiple linear regression method.关键词
高光谱数据/RBF神经网络/草地叶面积指数/反演Key words
hyper - spectral data/RBF neural network/LAI of grassland/retrieval分类
信息技术与安全科学引用本文复制引用
包刚,覃志豪,周义,包玉海,辛晓平,红雨,海全胜..基于高光谱数据和RBF神经网络方法的草地叶面积指数反演[J].国土资源遥感,2012,(2):7-11,5.基金项目
国家重点基础研究发展计划(973计划)项目(编号:2010CB951504)、国家自然科学基金(编号:41161060和41161086)、中国农业科学院呼伦贝尔草原生态系统国家野外科学观测研究站开放基金项目(编号:2010-05)和内蒙古自治区高等学校科学研究项目(编号:NJ10169)共同资助. （973计划）
