With the increasing demand for high-resolution images from earth observation satellites, there is a need to optimize the usability of the images being downloaded in the ground stations. Most captured satellite images are not usable for certain applications due to high cloud cover percentage. To address this problem, this research demonstrates a cloud detection and dynamic attitude correction evaluation software. This software explores two key experiments. First is evaluating different image processing and machine learning-based approaches to detect cloud cover. The cloud detection algorithms were evaluated based on their accuracy, latency, and memory consumption. The second is exploring dynamic attitude correction to minimize the effect of cloud cover on captured images. Results show that our software can help test algorithms that increase the usability of captured images
