A
Aliaksei Makarau
Researcher at German Aerospace Center
Publications - 33
Citations - 496
Aliaksei Makarau is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Multispectral image & Sensor fusion. The author has an hindex of 10, co-authored 33 publications receiving 387 citations. Previous affiliations of Aliaksei Makarau include Radboud University Nijmegen.
Papers
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Journal ArticleDOI
Haze Detection and Removal in Remotely Sensed Multispectral Imagery
TL;DR: The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data.
Journal ArticleDOI
Adaptive Shadow Detection Using a Blackbody Radiator Model
TL;DR: This paper presents an alternative robust method for shadow detection based on the physical properties of a blackbody radiator that adaptively calculates the parameters for a particular scene and allows one to work with many different sensors and images obtained with different illumination conditions.
Journal ArticleDOI
The new hyperspectral sensor desis on the multi-payload platform muses installed on the iss
R. Muller,Janja Avbelj,Emiliano Carmona,Andreas Eckardt,Birgit Gerasch,L. Graham,Burghardt Günther,Uta Heiden,Jack Ickes,Gregoire Kerr,Uwe Knodt,David Krutz,Harald Krawczyk,Aliaksei Makarau,R. Miller,R. Perkins,Ingo Walter +16 more
TL;DR: In this paper, the authors describe the Space Segment consisting of the MUSES platform and the instrument DESIS as well as the activities at the two synchronized ground segments consisting of processing methods, product generation, data calibration and product validation.
Proceedings ArticleDOI
MACCS-ATCOR joint algorithm (MAJA)
Vincent Lonjou,Camille Desjardins,Olivier Hagolle,Beatrice Petrucci,Thierry Tremas,Michel Dejus,Aliaksei Makarau,Stefan Auer +7 more
TL;DR: In this paper, the authors presented a multi-mission Atmospheric Correction and Cloud Screening (MACCS) software for detecting clouds based on image time series to characterize the atmosphere and detect clouds.
Journal ArticleDOI
Combined Haze and Cirrus Removal for Multispectral Imagery
TL;DR: The HTM method is substantially improved by employing the 1.38-μm cirrus band, which restores the information in highly inhomogeneous surfaces attenuated by a low-altitude haze and high-altitudes cirrus, improving the interpretation of the scene content while preserving the shape of the spectral signatures.