基于Landsat 8 OLI影像纹理特征的面向对象土地利用/覆盖分类

针对如何提高中低分辨率遥感影像分类精度,该研究以河北省石家庄市Landsat 8 OLI遥感影像为研究对象,对灰度共生矩阵(gray-level co-occurrence matrix,GLCM)纹理与伽博(Gabor)滤波器下的Gist纹理特征进行对比,应用J-M(Jeffries-Matusita)距离可分离性分析GLCM最优纹理特征,并利用最佳指数法(optimum index factor,OIF)获取GLCM与Gist纹理特征的最佳特征组合;其次对面向对象分类的分割尺度进行研究,提出整体最优分割尺度计算方法;最后进行基于纹理特征的面向对象分类识别与精度评价,并与基于原始数据的面向对象分类结果进行对比。研究表明:Gist纹理特征使分类精度有了一定的提高,基于纹理数据的面向对象支持向量机(support vector machine,SVM)分类及面向对象K邻近法(K-nearest neighbor,KNN)分类的总体分类精度(overall accuracy,OA)分别比基于原始数据的2种方法分类精度提高3.67和3.33个百分点,基于纹理的面向对象SVM方法具有最高的精度,OA达到85.67%。不管是基于原始数据还是纹理数据,面向对象分类精度远高于最大似然分类(maximum likelihood classification,MLC)、马氏距离分类(mahalanobis distance classification,MDC)和SVM分类精度,且面向对象分类方法对纹理数据更为敏感。该文提出的基于纹理的面向对象分类方法有效提高了遥感影像分类精度,为区域土地利用/覆盖信息提取提供了有效的途径。

Object-oriented land use/cover classification based on texture features of Landsat 8 OLI image

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 4% and 4.34%, 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.