In remote sensing images, large areas of thin clouds can obscure ground object information, which has a great impact on subsequent interpretation and application of the images. In order to eliminate the influence of thin clouds on the underlying surface in satellite images, an algorithm for thin cloud detection and removal in multispectral images is developed. In the algorithm, the blue-green bands is used to synthesize extrapolated bands firstly, and then cloud thickness map (HTM) and thin cloud mask map are generated through dark pixelsearch, thereby obtaining cloud-free area pixel and cloud area pixel. Secondly, the HTM of each band is calculated, then both of the HTM of the extrapolated band and the HTM of each band are used to obtain the linear regression coefficient of each band. Finally, the images are subjected to thin cloud removal based on the coefficients. The method is applied to the multispectral images of Huanjing Jianzai-2A/B (HJ-2A/B) satellites. The results show that for different surface types, the image quality is significantly improved after removing thin clouds, and the ground object information covered by thin clouds can be clearly displayed, without affecting the image quality of cloud-free areas at the same time. After using this algorithm to remove thin clouds, the clarity, contrast and standard deviation of the multispectral images can be significantly improved, which provides quality assurance for subsequent image applications.