Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 469-473, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-469-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
09 Jun 2016
CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES
D. Cerra, J. Bieniarz, R. Müller, and P. Reinartz German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weling, Germany
Keywords: Sentinel-2, Clouds, Image Time Series, Image processing, Pre-processing Abstract. In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.
Conference paper (PDF, 5353 KB)


Citation: Cerra, D., Bieniarz, J., Müller, R., and Reinartz, P.: CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 469-473, https://doi.org/10.5194/isprs-archives-XLI-B3-469-2016, 2016.

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