To address the issue that traditional cloud detection algorithms are complex to differentiate thin from thick clouds and enhance the accuracy of remote sensing image cloud detection, a remote sensing image cloud detection algorithm with a dual attention mechanism is proposed. First, a dual attention model is constructed using the DenseNet structure, and dense connection modules are added to minimize the number of feature channels. Second, the global context module is introduced to obtain the global context information and further improve the system's performance. Finally, the cascading cavity convolution module is introduced to increase the receptive field and obtain more global image information. The experimental findings demonstrate that the proposed approach outperforms F-CNN, self-contrast, RF, SVM, and Fmask in both thin and thick cloud detection. As cloud pixels have a comprehensive detection accuracy of 0.9340, a low error rate of 0.0385, and a low false positive rate of 0.0693, over detection may be successfully avoided.