首页 | 本学科首页   官方微博 | 高级检索  
     

基于遗传BP神经网络算法的主被动遥感协同反演土壤水分
引用本文:余凡,赵英时,李海涛. 基于遗传BP神经网络算法的主被动遥感协同反演土壤水分[J]. 红外与毫米波学报, 2012, 31(3): 283-288
作者姓名:余凡  赵英时  李海涛
作者单位:1. 中国测绘科学研究院地理空间信息工程国家测绘局重点实验室,北京100830;中国科学院研究生院资源与环境学院,北京100049
2. 中国科学院研究生院资源与环境学院,北京,100049
3. 中国测绘科学研究院地理空间信息工程国家测绘局重点实验室,北京,100830
基金项目:国家重点基础研究发展计划(973计划),中国测绘科学研究院科研基本业务经费(7771023)
摘    要:提出了一种基于遗传神经网络算法的主被动遥感协同反演地表土壤水分的方法.首先,建立一个BP神经网络,并采用遗传算法对BP网络的节点权值进行了优化.然后分别将TM数据(TM3,TM4,TM6)、不同极化和极化比的(VV,VH,VH/VV)ASAR数据作为神经网络的输入,土壤水分含量作为网络的输出,用部分实测数据对网络进行训练并反演得到研究区土壤水分布图.最后,利用地面实测数据分别对遗传神经网络优化算法的有效性和主被动遥感协同反演的效果进行了验证,结果表明,新优化算法是有效可行的,且TM和ASAR协同反演的结果比两者单独反演的结果明显要好,体现了主被动遥感协同反演土壤水分的优势与潜力.

关 键 词:主被动遥感  GA-BP神经网络  土壤水分  反演
收稿时间:2011-06-21
修稿时间:2011-05-31

Soil moisture retrieval based on GA BP neural networks algorithm
YU Fan,ZHAO Ying-Shi and LI Hai-Tao. Soil moisture retrieval based on GA BP neural networks algorithm[J]. Journal of Infrared and Millimeter Waves, 2012, 31(3): 283-288
Authors:YU Fan  ZHAO Ying-Shi  LI Hai-Tao
Affiliation:Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying & Mapping,College of Resource and Environment, Graduate School of the Chinese Academy of Science,Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying & Mapping
Abstract:active andA new semi empirical model is presented for soil moisture content retrieval, using ENVISAT ASAR and LANDSAT TM data collaboratively. Firstly, a back propagation(BP) neural network algorithm(GA) is introduced, and a genetic algorithm is applied to optimize the weights of the node of BP neural network. Then the TM bands (TM3, TM4, TM6) and ASAR data(VV, VH, VH/VV) are taken as the input of the GA BP neural network, and the output corresponds to the ground soil moisture. The partial field measurements of soil moisture are used as training samples to train the network and to achieve the map of soil moisture distribution. The field measurements are used to test the validity of the BP neural network algorithm and effectiveness of the active and passive remote sensing cooperative inversion. The comparison between the inversion using single data set(TM or ASAR), and the cooperative inversion of active and passive remote sensing data demonstrates that the new algorithm is more effective, and shows considerable potential in soil moisture retrieval by integrating active and passive remote sensing data. passive remote sensing; GA BP neural network; soil moisture; inversion
Keywords:active and passive remote sensing   GA-BP Neural network   soil moisture   inversion  
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《红外与毫米波学报》浏览原始摘要信息
点击此处可从《红外与毫米波学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号