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In order to improve the accuracy of apple orchard extracting in very high spatial resolution (VHSR) remote sensing image, an automated apple orchard extracting method based on texture features together with spectral values and support vector machine (SVM) was studied. This method firstly obtained the optimum combination of multi-spectral bands by using the optimum index factor (OIF);then three kinds of texture features, namely gray level co-occurrence matrix (GLCM), fractal and spatial autocorrelation texture with six different window sizes (from 3 pixels×3 pixels to 13 pixels×13 pixels) were extracted from the panchromatic image for comparison, and further merged with spectral values respectively;finally the above features were used to identify apple orchard by using SVM. Experiments using QuickBird data showed that spectral features combined with texture features could achieve higher apple orchard extraction accuracy (Fa) and overall accuracy (OA) than using spectral features or textures features alone. Among the different features used, the spectral+GLCM features (with 9 pixels×9 pixels) achieved the highest accuracy (Fa and OA were 96.99% and 96.16%, respectively), which were slightly higher (0.63 and 1.56 percentages, respectively) than those of spectral+fractal features and significantly higher (11.92 and 9.20 percentages, respectively) than those of spectral+spatial autocorrelation features. Among the different classification methods, three classification techniques (SVM, maximum likelihood and neural networks) were compared for accuracy in apple orchard detection, and results suggested that SVM had the highest accuracy in identifying apple orchard. McNemar test was also computed for statistic significance among spectral+GLCM and other features and also among the three classifiers, and the confidence levels were all less than 5%. Consistency of the extracted apple orchard area and the visual interpretation results according to filed investigation and Google Earth VHSR concurrent image were able to achieve 98% in test regions.