Cloud is a large obstacle to remote sensing image processing and analysis. In order to solve this problem, an optimizational cloud detection algorithm is proposed for GF-1 satellite image based on the spectral and textural information. In the cloud-like areas detected by spectral analysis, a new sub-image segment method and dynamic threshold is used to improve the accuracy of texture detection. The Otsu algorithm is used to restore the thick cloud boundary information, since neither the fixed spectral threshold setting nor textural detection can get the boundaries of cloud in a complex environment. The results show that this method can effectively detect the cloud cover in the remote sensing image, optimally extract thick cloud boundary information, and effectively separate thin cloud and thick cloud.