Abstract: The extraction of textural features gives more information in many pattern recognition issues. The textural features are widely exploited in the classification problems. In this study, the high resolution images were employed as the research object.Textural features and spectral features were combined to solve the problems of land cover classification. The paper designed a classification strategy based on Fourier spectrum texture. The spectral image was submitted to the principal components analysis (PCA) and r-spectrum was extracted from the first two principal components as the textural features. The common power spectrum might be insufficient because of the lack of the feature number. However, it would bring redundancy when taking into account each sample spectrum associated with each frequency. In fact, it could be divided and quantified flexibly. Consequently, new features were yielded through the method. In this study, different scenarios associated with different input features were designed. Support vector machine (SVM) was employed as the classifier. The algorithm was tested on hyperspectral dataset acquired in Salinas area and QuickBird images acquired in Jiufeng area. Results showed that the combination of textural features and spectral features could obviously improve the accuracy; the textural features extracted by Fourier r-spectrum could be applied well to the classification problems of high resolution remote sensing images; the classification accuracy was higher than the one that is based on the whole sample spectrum and the commongray-level co-occurrence matrix (GLCM); it was suitable to move the window pixel by pixel when extracting the textural features; adaptive weight would better deal with the problems of multiple features; the textural features extracted from the first and second principle components had complementary properties. In addition, the extraction of textural information from multi-feature pictures was superior to single feature picture.