Deep Learning-Based Cloud Detection in High-Resolution Satellite Imagery Using Various Open-Source Cloud Images

Cloud cover is a significant obstacle to use optical satellite imagery. Therefore, various studies have been proposed to accurately detect clouds and evaluate satellite image quality. In particular, with the advancement of deep learning technology, many cloud detection studies are being conducted. However, a large volume of high-quality data is required to develop an effective deep learning model training. Thus, in this study, we compare the performance of deep learning cloud detection models for according to the diversity of sensors and resolutions of training data. For conducting the study, five case dataset combinations were constructed and trained with HRNet (High-Resolution Network). The performance evaluation of the trained models was conducted using test images from the KOMPSAT and PlanetScope satellites. As a mean of achieving high cloud detection results, it was found that selecting and using high-quality data is more effective than simply increasing the number of training data.