Cloud restoration of optical satellite imagery using time-series spectral similarity group - Yerin Yun; Jinha Jung; Youkyung Han (GIScience & Remote Sensing, 2024)
According to climate statistics, clouds cover more than a third of the Earth’s land surface on average. This cloud coverage obstructs optical satellite imagery, resulting in a loss of essential information concerning the surfaces beneath the clouds. Thus, various studies have been conducted to develop techniques for removing clouds from images. However, most of these studies have relied on non-cloud images as reference data, and radiometric differences have been identified as a factor that degrades the quality and accuracy of restoration images. To address these challenges, we propose the time-series spectral similarity group (TSSG) method for reconstructing cloud regions in satellite imagery using multi-temporal data. Prior to restoring the cloud areas in images, we employ Landsat quality assessment (QA) bands for cloud detection using redefined criteria. Subsequently, the TSSG algorithm uses time-series data to select reference images, overcoming the limitations of previous restoration methods by identifying pixels with locations of similar spectral values in the reference images. Using multi-temporal reference images, the TSSG method extracts pixels based on similar spectral values. This extraction enables the estimation of the true ground surface values by using the corresponding locations of similarity group pixels in cloud images. Simulated cloud images were used to assess the performance of the proposed cloud region restoration method. According to quantitative evaluations, the structural similarity index (SSIM) values representing image quality indicate that the proposed algorithm exhibits a higher restoration accuracy, with an average of 0.89, compared to the previous research having an average of 0.52. To further evaluate the visual impact of our approach, we applied the TSSG method to real cloud images. In addition, we conducted an in-depth analysis of various algorithm parameters to identify the optimal factors that yielded the most effective results. We focused on three key variables in terms of image quality indices: the number of reference images, the proportion of clouds within these references, and the proportion of similarity group pixels within the entire image. This study indicates the potential to enhance the utility of satellite imagery significantly by providing reliable data for time-series analysis, thus expanding the range of image applicability.