High Resolution Remote Sensing Image Classification in Hunchun Area Based on Object Oriented
In this paper,Chunhua Town,Hunchun City,Jilin Province,as the study area,with GF-1 and ZY-3 and Pleiades sat ellite image of the experimental data.Completed using object-oriented method for high-resolution remote sensing images to extract information.Using Filter 3D edge detection operator to optimize the multi-scale segmentation,and get the optimal segmentation parameters of the ground object,and establish the different elements of the division level.Characteristics of experimental data to build their classification hierarchy,select the characteristics of each feature elements of feature combinations,using a threshold value classification and fuzzy classification realize feature extraction feature information.The overall classification accuracy and kappa coefficient of the three images are obtained by using the confusion matrix.The analysis results show that the accuracy of Pleiades image classification is the highest,and it is more suitable for the extraction of image information in the experimentation area.
NETL
NSTL
万方数据
维普
