Due to the limited angle of photography, some natural images are oscured by fence-like occlusion such as barbed wire, fence and glass joints. A novel fence-like occlusion detection algorithm was proposed to repair such images. Firstly, aiming at the drawbacks of the existing fence detection algorithms using single pixel color feature and fixed shape feature, the image was divided into super pixels and a joint feature of color and texture was introduced to describe the super pixel blocks. Thus, the classification of a pixel classification problem was converted to a super pixel classification problem, which inhibited the misclassification caused by local color changes. Secondly, the super-pixel blocks were classified by using the graph cuts algorithm to extend the mesh structure along the smooth edges without being restricted by the fixed shape, which improved the detection accuracy of the special-shaped fence structure and avoided the manual input required by the algorithm proposed by Farid et al. (FARID M S, MAHMOOD A, GRANGETTO M. Image de-fencing framework with hybrid inpainting algorithm. Signal, Image and Video Processing, 2016, 10(7):1193-1201) Then, new joint features were used to train the Support Vector Machine (SVM) classifier and classify all non-classified super-pixel blocks to obtain an accurate fence mask. Finally, the SAIST (Spatially Adaptive Iterative Singular-value Thresholding) inpainting algorithm was used to repair the image. In the experiment, the obtained fence mask retained more detail than that of the algorithm proposed by Farid et al., meanwhile using the same repair algorithm, the image restoration effect was significantly improved. Using the same fence mask, restored images by using the SAIST algorithm are 3.06 and 0.02 higher than that by using the algorithm proposed by Farid et al., respectively, in Peak Signal-to-Noise Rate (PSNR) and Structural SIMilarity (SSIM). The overall repair results were significantly improved compared to the algorithm proposed by Farid et al. and the algorithm proposed by Liu et al. (LIU Y Y, BELKINA T, HAYS J H, et al. Image de-fencing. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2008:1-8) when using the SAIST inpainting algorithm combined with the proposed fence detection algorithm. The experimental results show that the proposed algorithm improves the detection accuracy of the fence mask, thus yields better fence removed image reconstruction.