Cloud masking of multitemporal remote sensing images

摘要

An automatic cloud masking is one of the first required processing steps since the operational use of satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions allows us to consider cloud screening as an unsupervised change detection problem in the temporal domain. Therefore, we propose a cloud screening method based on detecting abrupt changes in the temporal domain. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes in certain spectral and spatial features will be mainly due to the presence of clouds. The method estimates the background and common surface changes using the full information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After cloud detection, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of the land cover dynamics and to generate more elaborated products. The proposed method is tested in a dataset with 5-day revisit time series from SPOT-4 at high resolution and Landsat-8 time series.