ANN Based High Spatial Resolution Remote Sensing Wetland Classification
RS(Remote Sensing)image classification based on ANN(Artificial Neural Network)is carried out with high spatial resolution images of the wetland,which is the most important ecological environment element within the land components.Wetland dynamic change monitoring is often built upon its classification result concerned here.The typical high spatial resolution image of the wetland in Nanjing is used as a study case by ANN method in comparison with MLC(Maximum Likelihood Classification).Furthermore,the optimal number of ANN hidden neurons are simulated for enhance the classification effectivity.Totally,the results show classification method of ANN with optimal hidden neurons can effectively distinguish ground objects and improve the classification accuracy.The overall accuracy of the ANN classification is up to 93%and the Kappa coefficient is over 0.89.
Wetland Classification Artificial Neural Network Remote Sensing High Spatial Resolution Image Hidden Neuron Number
KE Zun-You AN Ru LI Xiang-Juan
School of Earth Sciences and Engineering,Hohai University,& Information Engineering Dept.,Nanjing In School of Earth Sciences and Engineering,Hohai University,Nanjing,PRC College of Business Administration,Nanjing University of Traditional Chinese Medicine Nanjing,PRC
国际会议
贵阳
英文
180-183
2015-08-18（万方平台首次上网日期，不代表论文的发表时间）