Dr. Aliaksei Makarau Profile

Dr. Aliaksei Makarau

at Deutsches Zentrum für Luft- und Raumfahrt eV

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Publications (4)

Proceedings Article | 19 October 2016 Presentation + Paper

MACCS-ATCOR joint algorithm (MAJA)

Vincent Lonjou, Camille Desjardins, Olivier Hagolle, Beatrice Petrucci, Thierry Tremas, Michel Dejus, Aliaksei Makarau, Stefan Auer

Proceedings Volume 10001, 1000107 (2016) https://doi.org/10.1117/12.2240935

KEYWORDS: Earth observing sensors, Atmospheric corrections, Clouds, Reflectivity, Photometry, Aerosols, Lanthanum, Sensors, Atmospheric particles, Landsat

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Proceedings Article | 23 October 2014 Paper

Spectrally consistent haze removal in multispectral data

Aliaksei Makarau, Rudolf Richter, Rupert Müller, Peter Reinartz

Proceedings Volume 9244, 924422 (2014) https://doi.org/10.1117/12.2070025

KEYWORDS: Air contamination, Landsat, Reflectivity, Aerosols, Satellites, Data acquisition, Solids, Calibration, Sensors, Earth observing sensors

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SPIE Journal Paper | 11 July 2012

Analysis and selection of pan-sharpening assessment measures

Aliaksei Makarau, Gintautas Palubinskas, Peter Reinartz

JARS, Vol. 6, Issue 01, 063548, (July 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063548

KEYWORDS: Image fusion, Image quality, Multispectral imaging, Earth observing sensors, High resolution satellite images, Landsat, Statistical analysis, Image classification, Sensors, Image processing

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Pan-sharpening of remote sensing multispectral imagery directly influences the accuracy of interpretation, classification, and other data mining methods. Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency, complexity of the pan-sharpening method, and other properties. The quality of a pan-sharpened image is assessed using quantitative measures. Generally, the quantitative measures for pan-sharpening assessment are taken from other topics of image processing (e.g., image similarity indexes), but the applicability basis of these measures (i.e., whether a measure provides correct and undistorted assessment of pan-sharpened imagery) is not checked and proven. For example, should (or should not) a quantitative measure be used for pan-sharpening assessment is still an open research topic. Also, there is a chance that some measures can provide distorted results of the quality assessment and the suitability of these quantitative measures as well as the application for pan-sharpened imagery assessment is under question. The aim of the authors is to perform statistical analysis of widely employed measures for remote sensing imagery pan-sharpening assessment and to show which of the measures are the most suitable for use. To find and prove which measures are the most suitable, sets of multispectral images are processed by the general fusion framework method (GFF) with varying parameters. The GFF is a type of general image fusion method. Variation of the method parameter set values allows one to produce imagery data with predefined quality (i.e., spatial and spectral consistency) for further statistical analysis of the assessment measures. The use of several main multispectral sensors (Landsat 7ETM+, IKONOS, and WorldView-2) imagery allows one to assess and compare available quality assessment measures and illustrate which of them are most suitable for each satellite.

Proceedings Article | 7 June 2011 Paper

Multisensor remote sensing information fusion for urban area classification and change detection

Gintautas Palubinskas, Aliaksei Makarau, Peter Reinartz

Proceedings Volume 8064, 80640F (2011) https://doi.org/10.1117/12.883767

KEYWORDS: Data fusion, Image fusion, Sensors, Buildings, Radar, Synthetic aperture radar, Data acquisition, Roads, Remote sensing, Neural networks

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