Object-oriented Classification and Information Extraction Based on High Spatial Resolution Remote Sensing Image

Abstract

The object-oriented classification method was used to extract land use/land cover information from high spatial resolution remote sensing images,and the results are compared with the traditional information extraction method.It showed that the accuracy was greatly improved,and the method can avoid salt and pepper noise effectively.The classification results are easier to understand and interpretative.Overall precision,Kappa,producer precision,user precision,Hellden precision and Short precision of object-oriented classification are better than that of traditional classification method.The extraction effect of each ground object increased significantly.overall precision increased 21.76% and Kappa coefficient increased 0.2756.These experiments show that the object-oriented method is superior to traditional method in high resolution remote sensing information extraction.