CLOUDSEGNET: A DEEP LEARNING BASED SEGMENTATION METHOD FOR CLOUD DETECTION IN MULTISPECTRAL SATELLITE IMAGERY

Cloud detection in satellite imagery is an essential and common preprocessing task in optical remote sensing image analysis. Helping minimize the dominant areal coverage of cloud in high-resolution satellite imagery, various space agencies operating optical remote sensing satellites consciously scout for temporal windows which offer the maximal probability of cloud-free days. Demanded by users across the globe, an acquisition plan for providing cloud-free imagery is critical for all space agencies. Despite many technological advancements, the dynamic nature of weather often poses cloud issues during the day, making the acquired imagery redundant. Thus, having an idea of the probability of an area being cloudy on a given date and time is very helpful in optimizing the resource deployment of space agencies, particularly for optical satellites. Therefore, this requires the acquisition development of long times series cloud image database with a significantly finer spatial resolution of up to 30 meters or better than that.