Open AccessCLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES
Author(s) -
Daniele Cerra,
J. Bieniarz,
R. Müller,
P. Reinartz
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-469-2016
Subject(s) - cloud computing , computer science , shadow (psychology) , pixel , satellite , series (stratigraphy) , artificial intelligence , iterative reconstruction , process (computing) , image restoration , image (mathematics) , computer vision , remote sensing , algorithm , image processing , geography , geology , psychology , paleontology , engineering , psychotherapist , aerospace engineering , operating system
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.