In order to investigate the distribution feature of surface texture, 168 images of Hami melon samples from two different varieties in two kinds of ripeness were acquired. The algebra operations were conducted in terms of R, G, B components, and the gray images were obtained to implement the background segmentation. Then, the images were decomposed by dual-tree complex wavelet transform(DT—CWT) to obtain high frequency sub-images. Following the neighborhood operation, the extraction results were derived from selecting the optimal thresholds by iterative method. Finally, the methods of gray-scale differential statistics and texture frequency analysis were used to analyze the texture feature, support vector machine(SVM) was employed to build a model for texture classification. Results of computer simulation indicated that more continuous and complete images were obtained when DT—CWT and image neighborhood operation were employed to extract texture. There were significant differences among texture eigenvalues of four types of Hami melons, and the accuracy rate of classification was 89.3%. In addition, periodic characteristic was not found from the appearance texture.