Abstract
Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.
Highlights
Clouds and the accompanying shadows are inevitable contaminants for high-resolution remote sensing images, which are widely used for urban geographical mapping, land-use classification, change detection [1,2]
In order to evaluate the performance of the proposed stepwise radiometric adjustment and residual correction (SRARC) method, we tested SRARC in a series of experiments, in which images with different spatial resolutions and land-cover change patterns were used for the accuracy assessment in both visual and quantitative manners
localized linear histograΩm match (LLHM) is a linear radiometric adjustment method which was originally utilized for gap filling in flawed Landsat Enhanced Thematic Mapper Plus (ETM)+ images, modified neighborhood similar pixel interpolator (MNSPI) combines spectro-spatial information and spectro-temporal information for the prediction of cloudy pixels, and WLR reconstructs missing pixels by weighted linear regression based on local similar pixels
Summary
Clouds and the accompanying shadows are inevitable contaminants for high-resolution remote sensing images, which are widely used for urban geographical mapping, land-use classification, change detection [1,2]. Cloud cover results in missing information and spatio-temporal discontinuity, and affects the precise application of time-series satellite images [3]. Reconstructing contaminated areas in the cloudy satellite image with the aid of close-date temporal images can help to increase the data usability, and can be used to generate cloud-free and spatio-temporally continuous images for time-series analysis, especially for areas heavily contaminated by clouds. Examples of applications that benefit from cloud removal include land-cover/land-use mapping, change detection, urban planning, etc. Cloud removal for optical satellite images is of great significance
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