With the support of a pre-calculated land surface reflectance database, the universal dynamic threshold cloud detection algorithm (UDTCDA) can significantly improve the cloud detection accuracy of satellite data. To further improve its precision in the application of cloud detection for high spatial-resolution satellite data with relatively few bands, we improve the spatial matching method between the prior surface reflectance and the satellite observed reflectance. Different with the directly resample method in the UDTCDA, the pixel-by-pixel registration method is adopted to realize the matching between the satellite image and surface reflectance image. This approach preserves the spatial resolution advantage of high resolution images, and effectively reduces the loss of pixel information caused by spatial resampling. Four high-resolution satellite data, namely ZY-3, GF-1, GF-2 and GF-4, are used in cloud detection experiments. The cloud detection results of the improved UDTCDA are verified by the visual interpretation cloud results, and compared with the original UDTCDA cloud results. Results show that the improved algorithm can accurately identify different kinds of clouds in different high-resolution satellite images with an average accuracy of 93.92%. Especially for the broken and thin clouds, the accuracy is significantly improved with overall low omission and commission errors less than 10.40% and 9.57%, respectively.