Due to their ease of access and low cost, unmanned aerial vehicle (UAV) visible remote sensing images have been widely used for the statistical analysis of agricultural resources. To obtain more representative features of UAV visible remote sensing images and achieve accurate land-cover classification, a land-cover classification algorithm based on the joint distribution of color-spatial features is proposed. First, the index of the golden rectangular patch is defined to select patches for sampling from the labeled data. Based on the golden rectangles of the selected patches, a logarithmic spiral was constructed to choose the training samples. Color feature reference points and neighborhood pixels were then applied to calculate the difference information and extract the color-space joint feature for each sample. Subsequently, the objective function of the joint feature is constructed using Jensen's inequality and fuzzy classification maximum likelihood. Next, the multidimensional mixed Weibull distribution of each sample is solved using several iterations. Finally, a similarity measure corresponding to the multidimensional mixed Weibull distribution was defined to classify each sample under analysis. Experimental results show that the overall accuracy of the proposed algorithm reaches 98.6%, which is better than that of local binary pattern, gray level cooccurrence matrix, random forest, ResNet, and VGG, proving the effectiveness of the proposed algorithm.