Abstract: Abstract: 【Objective】 Developing cloud detection technology for high-resolution remote-sensing images is difficult, especially for thin edge and scattered clouds. We used morphological operations and polygon simplification techniques to accurately extract cloud-containing, high-resolution remote-sensing images.【Method】First, the Gaussian low-pass filter was used to smooth the image and create uniform shading. The image was then divided into three categories: cloudy, partly cloudy and cloudless. The Otsu threshold method was used on cloudy images, and Gaussian mixture model segmentation was used on partly cloudy images. Finally, the cloud area was morphologically processed to determine the final cloud area. 【Result】The high-resolution remote-sensing image cloud-detection algorithm based on Otsu threshold segmentation and Gaussian mixture model has good visual effect and can effectively improve the accuracy of cloud detection. The accuracy rate is 98.60%, recall rate of the method is approximately 90%, and the error rate is approximately 2.58%. It can accurately detect thick, thin and scattered clouds, and can also effectively reduce the misidentification of houses, roads and bare land. 【Conclusion】The high-resolution remote-sensing image cloud-detection algorithm based on Otsu threshold segmentation and Gaussian mixture model is moderately complex, and has a small computation size, fast operation speed, high-precision detection and wide applicability.