Rapidly Single-Temporal Remote Sensing Image Cloud Removal based on Land Cover Data

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images. To rapidly acquire noncloud images, we design a cloud removal method to recover single-temporal remote sensing image based on land cover data which is easier to obtain than multitemporal data. Considering that the same features have the same radiation characteristics, we extract the similar pixels from same category around the missing pixels and calculate the value of missing pixels according to the distance weights of these pixels. The performance of the proposed method was evaluated on MODIS images and Landsat images and the results also prove that universal applicability of this algorithm in different resolutions and surface contents.