Virtual image-based cloud removal for Landsat images

ABSTRACT

The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free image(s) in the same area acquired on other date(s) as reference image(s) to reconstruct missing pixel values. However, it remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image within cloud region construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e. urban, croplands, and wetlands) and land change patterns (i.e. phenological change, abrupt change caused by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved higher Correlation Coefficients and lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from the reference images, TWSD can select more reliable similar pixels to improve the prediction of abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally much faster than ARRC. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

KEYWORDS:

• Cloud removal
• Time-series images
• Landsat
• Similar pixels
• Gap filling
• Thick clouds

Acknowledgments

The work is supported by the National Natural Science Foundation of China [grant number 42071396].

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The data that support the findings of this study are available upon request by contact with the corresponding author, or accessed through https://earthexplorer.usgs.gov.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2022.2160411

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42071396].