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Objective: The existence of cloud reduces the application value of remote sensing images. Accurate and automatic cloud and cloud shadow detection and labeling for multispectral satellite images is conducive to the subsequent application of remote sensing images. China currently has a large number of high-resolution multi-spectral satellite images, but standard data products rarely contain pixel-by-pixel cloud and cloud shadow tag data for quality analysis. Traditional cloud detection algorithms usually require parameters such as satellite imaging geometry, imaging time, and calibration coefficients, but a lot of Chinese satellites’ images have lost parameter auxiliary files during multiple product iterations, and many military application satellite images are missing or do not provide parameter files. Multispectral satellite image cloud and cloud shadow detection with missing parameters require special researches. Method: This paper studies the cloud and cloud shadow detection method of domestic four-band multi-spectral satellite imagery with missing related parameters. This algorithm process is based on the classic spectral threshold cloud and cloud shadow detection algorithm, and uses image processing and morphological algorithms to improve detection accuracy. For the data with missing parameters, a morphology-based method for estimating the azimuth and distance of the cloud shadow relative to the cloud area is proposed. Result: The experimental data in this paper is from the Gaofen-1 (GF-1) satellite WFV sensor, and the 86 test images are from Dunhuang, Gansu, China. The experimental area contains a large area of bright surface and snow-capped mountains that are easy to be misdetected in cloud and cloud shadow detection. The result of the cloud and cloud shadow detection experiment of this paper in the case of missing parameters achieves similar accuracy to normal algorithms. At the same time, this paper analyzes the misdetection of the algorithm and clarifies the challenges of subsequent researches. Conclusion: In this paper we propose a set of refined cloud and cloud shadow detection algorithms in case of missing parameters for domestic four-band multi-spectral satellite imagery. The algorithms are based on the classical spectral threshold cloud and cloud shadow detection algorithm and use image processing and morphological algorithms to further improve accuracy. Moreover, a morphology-based method for estimating the orientation and distance of cloud shadow relative to the cloud area is proposed for the data with missing parameters. The experimental results of GF-1 WFV data show that in the case of missing parameters, the detection results of this algorithm achieve an accuracy similar to that of the widely used MFC algorithm.