Land Use/Cover Classification Of Landsat 8 OLI Remote Sensing Images Based On Texture And Object-Oriented Method

Remote sensing image classification is the main approach for rapidly obtaining regional land use/cover information and it has always been an important part in the field of remote sensing.How to improve the classification accuracy of remote sensing images with medium and low spatial and spectral resolution is an urgent problem to be solved in remote sensing research.In this paper,we took the Shijiazhuang Landsat 8 OLI remote sensing image data as the research area,and systematically studied object-oriented classification based on the spatial texture features of remote sensing images.Firstly,overview of object-oriented classification and using edge-based segmentation methods to segment remote sensing images,meanwhile,the concept of ?the optimal overall segmentation scale? was proposed,which was based on the ratio between total area and the number of the objects in classification result.Secondly,selecting appropriate texture features to build the texture feature set of this paper.Gray Level Co-occurrence Matrix(GLCM)texture features and Gist texture features which were based on Gabor filter were compared and analyzed.The average J-M distance methods were used to evaluate the sample separability and to choose optimal texture features of GLCM.Subsequently,the optimum index factor was applied to obtain the best combination of the two texture features.Finally,two object-oriented classification methods,K-Nearest Neighbor(KNN)method and Support Vector Machine(SVM)method,were used to classify the texture data and the original data,and the accuracy of assessment results were compared with which using three traditional supervised classification methods.The experimental results indicate that the fusion of texture features could improve the accuracy of classification to some extent.The overall classification accuracy based on texture data using object-oriented SVM and object-oriented KNN had increased compared with the results based on original data.Although the classification accuracy of the texture-based supervised classification had been improved compared with the supervised classification based on original data,the accuracy was far lower than the value with object-oriented method.In summary,the texture feature has positive effect on improving the accuracy of remote sensing classification.The research method in this paper not only gives a valuable reference for other kinds of remote sensing images,but also provides an effective approach for the extraction of regional land use/cover information.