Dr. Gintautas Palubinskas Profile

Dr. Gintautas Palubinskas

Research Scientist at Deutsches Zentrum für Luft- und Raumfahrt eV

SPIE Involvement:

Author

Area of Expertise:

data fusion , remote sensing , SAR , optical imagery , information extraction , classification

Websites:

Company Website

Publications (13)

SPIE Journal Paper | 9 August 2013 Open Access

Fast, simple, and good pan-sharpening method

Gintautas Palubinskas

JARS, Vol. 7, Issue 01, 073526, (August 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073526

KEYWORDS: Image fusion, Quality measurement, Image quality, Linear filtering, Image filtering, Electronic filtering, Filtering (signal processing), Remote sensing, Data fusion, Optical filters

Read Abstract +

DOWNLOAD PAPER

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

Read Abstract +

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

Read Abstract +

Proceedings Article | 19 October 2005 Paper

Automatic extraction of runway structures in infrared remote sensing image sequences

Christian Stephan, Gintautas Palubinskas, Rupert Müller

Proceedings Volume 5982, 598208 (2005) https://doi.org/10.1117/12.626391

KEYWORDS: Infrared imaging, Infrared sensors, Synthetic vision, Quality measurement, Infrared cameras, Sensors, Image fusion, Image processing, Cameras, Infrared radiation

Read Abstract +

Proceedings Article | 19 October 2005 Paper

Estimation of along-track velocity of road vehicles in SAR data

G. Palubinskas, F. Meyer, H. Runge, P. Reinartz, R. Scheiber, R. Bamler

Proceedings Volume 5982, 59820T (2005) https://doi.org/10.1117/12.626785

KEYWORDS: Frequency modulation, Fermium, Synthetic aperture radar, Image processing, Radar, Sensors, Data acquisition, Global Positioning System, Roads, Error analysis

Read Abstract +

Proceedings Article | 12 October 2005 Paper

Radar signatures of road vehicles: airborne SAR experiments

G. Palubinskas, H. Runge, P. Reinartz

Proceedings Volume 5980, 598006 (2005) https://doi.org/10.1117/12.626783

KEYWORDS: Radar, Synthetic aperture radar, Roads, Data acquisition, Sensors, Polarization, Satellites, Velocity measurements, Algorithm development, Interferometry

Read Abstract +

Proceedings Article | 2 August 2002 Paper

Empirical radiometric correction of optical remote sensing imagery

Gintautas Palubinskas, Rupert Mueller, Peter Reinartz

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478741

KEYWORDS: Sensors, Radiometric corrections, Atmospheric optics, Bidirectional reflectance transmission function, Remote sensing, Atmospheric corrections, Radio optics, Atmospheric sensing, Scanners, Image sensors

Read Abstract +

Proceedings Article | 16 July 2002 Paper

Image processing in an enhanced and synthetic vision system

Rupert Mueller, Gintautas Palubinskas, Hans Gemperlein

Proceedings Volume 4713, (2002) https://doi.org/10.1117/12.474422

KEYWORDS: Sensors, Infrared imaging, Image sensors, Synthetic vision, Image processing, Navigation systems, Infrared sensors, Data processing, Enhanced vision, Forward looking infrared

Read Abstract +

Proceedings Article | 28 August 2001 Paper

IR measurements and image processing for enhanced-vision systems in civil aviation

Kurt Beier, Jochen Fries, Rupert Mueller, Gintautas Palubinskas

Proceedings Volume 4363, (2001) https://doi.org/10.1117/12.438024

KEYWORDS: Infrared imaging, Sensors, Image sensors, Synthetic vision, Infrared cameras, Infrared sensors, Cameras, Image processing, Image fusion, Data processing

Read Abstract +

Proceedings Article | 4 December 1998 Paper

K-means clustering algorithm using entropy

Gintautas Palubinskas

Proceedings Volume 3500, (1998) https://doi.org/10.1117/12.331894

KEYWORDS: Bayesian inference, Statistical analysis, Databases, Magnetism, Remote sensing

Read Abstract +

Proceedings Article | 20 November 1998 Paper

Multiscale Bayesian height estimation from InSAR using a fractal prior

Mihai Datcu, Gintautas Palubinskas

Proceedings Volume 3497, (1998) https://doi.org/10.1117/12.331347

KEYWORDS: Fractal analysis, Data modeling, Interferometric synthetic aperture radar, Stochastic processes, Statistical modeling, Interferometry, Motion models, Inverse problems, Process modeling, Data centers

Read Abstract +

Proceedings Article | 17 November 1995 Paper

Back-propagation networks for classification in remote sensing

Gintautas Palubinskas

Proceedings Volume 2579, (1995) https://doi.org/10.1117/12.226851

KEYWORDS: Synthetic aperture radar, Neurons, Remote sensing, Error analysis, Image classification, Pattern recognition, Neural networks, Detection and tracking algorithms, Agriculture, Data acquisition

Read Abstract +

Proceedings Article | 17 August 1994 Paper

Comparison of texture-based and fuzzy classification approaches for regenerating tropical forest mapping using LANDSAT TM

Gintautas Palubinskas

Proceedings Volume 2357, (1994) https://doi.org/10.1117/12.182862

KEYWORDS: Image classification, Fuzzy logic, Landsat, Volume rendering, Mahalanobis distance, Algorithm development, Remote sensing, Matrices, Scene classification, Earth observing sensors

Read Abstract +

Showing 5 of 13 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years