Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal

Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     

 

Keywords: Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN