Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images

Laser footprint images (LFIs) are auxiliary sensors of satellite laser altimeters, which are mainly used to determine whether the laser pulse is obscured by clouds, and establish a link between the laser and the high-resolution image, which requires an algorithm for achieving high-recognition accuracy while preserving the edge contour features of clouds. This study uses the AdaBoost algorithm to achieve the fusion of multiple semantic segmentation models and combines the dark channel prior (DCP) model to enhance the overall cloud detection effect, realize the removal of thin clouds, and improve the matching accuracy of footprint and altimeter images. The experimental results show that the model outperforms the basic cloud detection model by approximately 14.89% and that the contours of the extracted cloud-covered area are more consistent with subjective human perceptions. Furthermore, the matching accuracy of the optimized footprint and high-resolution altimeter images improved by approximately 17.67%.