Abstract:
Abstract: 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 is an urgent problem to be solved in remote sensing research. In traditional classification, only the spectral features of remote sensing image are used, while the texture and other features are ignored. Therefore, it is very common to see the object confusion in the classification result. 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, Gray Level Co-occurrence Matrix (GLCM) texture features and Gist texture features based on Gabor filter were compared and analyzed. The average J-M distance method was used to evaluate the sample separability and to choose optimal texture features of GLCM. Subsequently, the optimum index factor (OIF) was applied to obtain the best combination of the two texture features. Secondly, the segmentation scale of object-oriented classification was studied in detail, meanwhile, the concept of “the optimal overall segmentation scale” was proposed, which was based on the ratio between maximum area and the number of the objects in classification result. 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 using three traditional supervised classification methods. The results indicated 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 increased by 3.67 and 3.33 percentage points, respectively, compared with the results based on original data. Object-oriented SVM method based on texture data had the highest classification accuracy with overall classification accuracy of 85.67%, and with Kappa index of 0.81. Although the classification accuracy of the texture-based supervised classification was improved compared with the supervised classification based on original data, the accuracy was far lower than the value with object-oriented method. For original data, the overall classification accuracy of object-oriented KNN increased by 4.33%, 3.99% and 2.00%, respectively, compared with Maximum Likelihood Classification (MLC), Mahalanobis Distance Classification (MDC) and SVM method. The overall classification accuracy of object-oriented SVM increased by 8.33%, 7.99% and 6.00%, respectively, compared with three supervised methods. After fusing Gist texture features, the overall classification accuracy of object-oriented KNN had increased by 6.33%, 4.66% and 2.66% respectively compared with MLC, MDC and SVM. Whereas the overall classification accuracy of object-oriented SVM increased by 10.67%, 9% and 7.00%, respectively, compared with three supervised methods. Object-oriented SVM method is more sensitive to texture features with the maximum increase of classification accuracy. In future study, more texture features need to be considered to extend the application range of remote sensing classification. In summary, the texture feature has positive effect on improving the accuracy of remote sensing classification, and the application of Gist textures have great potential in object-oriented classification. Moreover, it can also be found that object-oriented method is suitable for classifying medium resolution remote sensing image. 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.