Journal
REMOTE SENSING OF ENVIRONMENT
Volume 179, Issue -, Pages 183-195Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2016.03.034
Keywords
Empirical analysis; Landsat-8; Radiative transfer model; Thin cloud and its removal; Visible bands
Funding
- Fundamental Research Funds for the Central Universities [ZYGX2013Z006]
- United States Geological Survey [G14AP00002]
- U.S. Department of the Interior to AmericaView/North CarolinaView and then to East Carolina University, North Carolina, USA
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An algorithm for cloud removal in visible bands was developed. Thin clouds inside visible Bands 1-4 of Landsat-8 data acquired on 8 January 2014 disappeared after the algorithm. Values of mean and one standard deviation decreased, band-by-band. The reduction was supported by the leftward shift of the histogram curve in each band. To validate the algorithm, we used the cloud-free image acquired on 23 December 2013 of Landsat-8 as the reference image. Among the January image before the algorithm, the January image after the algorithm, and the reference image, values of mean and one standard deviation of the January image after the algorithm were much closer to those of the reference image. Histogram curves of the January image after the algorithm and the reference image were almost overlapped entirely. Spatial correlation coefficients of the January image before the algorithm and reference were 0.496, 0.547, 0.656, and 0.730 for Bands 1-4, respectively. Coefficients of the January image after the algorithm and reference image became 0.782, 0.822, 0.840, and 0.885 for Bands 1-4. In cloud-free areas, the algorithm did not alter spectral characteristics of cloud-free pixels. Thus, the algorithm was not only able to remove thin clouds, but also to preserve spectral characteristics of cloud-free pixels. The algorithm was then applied to other land use and land cover (LULC) types, and images acquired in other locations and seasons by Landsat-8 and WorldView-2 sensors. Results in cloud removal were satisfactory. Finally, this algorithm outperformed three widely-used cloud removal algorithms in comparison. (C) 2016 Elsevier Inc. All rights reserved.
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