Reconstruction of cloud contaminated information in optical satellite images

Abstract

Optical satellite images are the main source of geophysical information which can be used for various land surface studies. But one of the most challenging issue faced by these images are the severe cloud contamination. As far as optical satellite images are of concern, thick clouds will completely obstructs the observation of landscape and uneven illumination caused by thin clouds will further limits the processing of optical satellite images. So a method to eliminate the impact of clouds in optical satellite images is necessary to improve the effectiveness and availability of remote sensing data for various land surface studies. In this paper, a novel methodology for removing the uneven illumination caused by thick and thin clouds is introduced. For this approach, multitemporal cloud contaminated optical satellite images are required with the assumption that in each image cloud cover change significantly over a short duration of time. A cloud detection algorithm in frequency domain is used to detect the cloud contaminated portions in multitemporal optical satellite images. Suitable cloud free patch from reference images for reconstructing cloud contaminated portions in target image are selected based on lower amount of cloud rate and quality assessment technique. Some experimental analysis is conducted on collected Aqua MODIS sensor captured multitemporal satellite images. Experimental result shows that the proposed algorithm can effectively remove majority of cloud contamination within the optical satellite images. Finally quantitative measures are performed to evaluate the feasibility of the methodology.

Cloud removal analysis of remote sensing satellite imagery

“…In the past decade, there were several approaches applied for cloud removal, such as cloning information (Lin et al, 2013), sparse representation (Huang et al, 2015), multi-temporal dictionary learning (Li et al, 2014;Li et al, 2017), reconstruction of cloud contaminated information (Veena and Kumar, 2014), and thin cloud removal from single satellite image (Liu et al, 2014). In these studies, they used their approaches in removing clouds over a few land cover types, whereas the cloud is difficult to be detected over heterogeneous land cover as mentioned above.…”