Xiaolin Zhu^1,2, Feng Gao³, Desheng Liu⁴, Jin Chen¹

¹. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

². Department of Geography, The Ohio State University, Columbus, OH 43210 USA

³. Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U. S. Department of Agriculture, Beltsville,MD20705 USA

⁴. Department of Geography and the Department of Statistics, The Ohio State University, Columbus, OH 43210 USA

 

Abstract: Thick-cloud contamination is a common problem in Landsat images, which limits their utilities in various land surface studies. This letter presents a new method for removing thick clouds based on a modified neighborhood similar pixel interpolator (NSPI) approach that was originally developed for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. The performance of the proposed method was evaluated with both simulated and real cloudy images and compared with that of a contextual multiple linear prediction (CMLP) method. The results show that the modified NSPI approach can greatly reduce the edge effects by CMLP. The reflectance restored by the modified NSPI approach is more accurate than that by CMLP, especially when the cloud-free auxiliary and cloudy images are acquired from different seasons and have different spectral characteristics.

 

Keywords: Cloud removal, image processing, image restoration, Landsat.