English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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