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Detection of shadows in high spatial resolution ocean satellite data using DINEOF
Alvera-Azcárate, A.; Van der Zande, D.; Barth, A.; Cardoso dos Santos, J.F.; Troupin, C.; Beckers, J.-M. (2021). Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sens. Environ. 253: 112229. https://hdl.handle.net/10.1016/j.rse.2020.112229
In: Remote Sensing of Environment. Elsevier: New York,. ISSN 0034-4257; e-ISSN 1879-0704
Peer reviewed article  
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Authors  Top 
  • Alvera-Azcárate, A.
  • Van der Zande, D.
  • Barth, A.
  • Cardoso dos Santos, J.F.
  • Troupin, C.
  • Beckers, J.-M.
Abstract
    Cloud shadows present in high spatial resolution remote sensing datasets can affect the quality of the data if they are not properly detected and removed. When working with ocean data, cloud shadows are often difficult to differentiate from non-shadow values, since they show similar spectral characteristics than water pixels. A methodology to detect cloud shadows over the ocean is proposed. The present approach combines a series of tests applied directly to the physical variables derived from the satellite measured radiances, and it therefore does not depend on the wavebands measured by a specific satellite sensor. The tests include a departure from an EOF basis calculated using DINEOF, a threshold test, a proximity to cloud test and a ray tracing test. The weighing of the different tests can be adapted to each case or domain of study. The results are compared to manually detected shadows and to another shadow detection method. The approach works with cloud shadows of all sizes, and also with very small objects shadows, like the shadows projected by offshore windmills.
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