Virtual image patch-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 images in the same area acquired on other dates as reference image(s) to reconstruct missing pixel values. Although it has been widely recognized that reference image(s) has great impacts on the performances of cloud removal algorithms, it still 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 patch-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image patch 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 cause 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 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 each reference image, TWSD improved the ability of VICR in predicting abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally faster than ARRC and mNSPI. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

“…To date, many cloud removal algorithms have been developed (Zhu et al, 2012a;Cao et al, 2020;Wang et al, 2022). These algorithms used one or more cloud-free imagery as reference images to predict the missing values in the cloud and cloud shadows in the target image (i.e., the cloud image).…”

“…These algorithms used one or more cloud-free imagery as reference images to predict the missing values in the cloud and cloud shadows in the target image (i.e., the cloud image). However, these algorithms had limited performance when dealing with landscapes with abrupt changes (Wang et al, 2022) such as the estuarian wetlands that are frequently inundated by tidal water.…”

“…VICR also proposed a strategy for time-series cloud image processing. Details of the model can be found in Wang et al (2022). Following this strategy, the Landsat imagery acquired in 2020 over the study area (Table 1) were sorted in the order of the cloud cover percentage; the image with the lowest cloud cover was processed first and then the cloud- Landsat 8 OLI good observations in the Yellow River Delta in 2020.…”

“…At present, many scholars have developed cloud removal algorithms for optical remote sensing images, which can reconstruct the reflectance of the land surface covered by thick clouds and cloud shadows (Zhu et al, 2012a;Chen et al, 2017;Cao et al, 2020). Our previous research found that the existing algorithms tended to produce poor reconstruction results over the coastal wetlands because the coastal wetlands are highly dynamic due to tidal inundation (Wang et al, 2022). Therefore, we proposed a new cloud removal algorithm, i.e., virtual imagebased cloud removal (VICR) algorithm (Wang et al, 2022), which improved the cloud removal accuracy over the coastal wetlands.…”

“…Our previous research found that the existing algorithms tended to produce poor reconstruction results over the coastal wetlands because the coastal wetlands are highly dynamic due to tidal inundation (Wang et al, 2022). Therefore, we proposed a new cloud removal algorithm, i.e., virtual imagebased cloud removal (VICR) algorithm (Wang et al, 2022), which improved the cloud removal accuracy over the coastal wetlands. We expect that the full time-series cloud-removed images reconstructed by VICR help to enhance the fractional cover estimation of saltmarsh vegetation at species level at the coastal wetlands.…”