Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Proceedings

Esch, Thomas²  Adam, Fathalrahman³  Mönks, Milena⁴  Datcu, Mihai⁵ 

[1] Available online: https://sciforum.net/conference/ecrs-2.;Author to whom correspondence should be addressed.;Earth Observation Center, German Aerospace Center(DLR), Münchner Str. 20, 82234 Weßling, Germany;Faculty of Mathematics and Natural Science, Institute of Biochemistry, Universität Greifswald, Felix-Hausdorff-Straße 4, 17487 Greifswald, Germany;Presented at the 2nd International Electronic Conference on Remote Sensing, 22 March–5 April 2018

关键词: cloud removal;    image reconstruction;    temporal fitting;    spectral matching;    radiometric interpolation;    L;    sat;   

DOI  :  10.3390/ecrs-2-05166

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Clouds and cloud-shadow are a persistent problem in all optical satellite imagery. Plenty of methods have been suggested in the literature to address this problem, and reconstruct the missing part of the optical signal. In this work, three methods representative of different approaches to the cloud removal problem were compared. The first method is temporal fitting using Fourier series, which benefits from the temporal continuity of the signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs radiometric consistency as a tool to determine the missing part of the signal. These three methods were first presented and their theoretical background described, followed by a discussion of their implied assumptions and general performance. A set of experiments using Landsat 8 time series with diverse land cover types were conducted. The quantitative results of the three methods using simulated clouds are presented. Finally, some concluding remarks about the relative advantages of the three approaches are listed, in addition to some recommendations about their use.

【 授权许可】

CC BY