Dr. Bernhard Rabus Profile

Dr. Bernhard Rabus

at Simon Fraser Univ

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Publications (4)

Proceedings Article | 20 September 2020 Presentation + Paper

SAR and optical image fusion for urban infrastructure detection and monitoring

Usman Iqbal Ahmed, Bernhard Rabus, Mirza Faisal Beg

Proceedings Volume 11535, 115350M (2020) https://doi.org/10.1117/12.2579480

KEYWORDS: Image fusion, Image segmentation, Synthetic aperture radar, RGB color model, Principal component analysis, Image enhancement, Image resolution, Planets, Image processing, Infrared imaging

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Spaceborne Synthetic Aperture Radar (SAR) and Optical sensors, are one of the main sources of Earth observation in the present age. Both the data types have their inherent advantages and disadvantages. Spaceborne Optical sensor are restricted by clouds but can offer strong information content in ideal conditions. On the other hand, SAR sensors rely on their own energy and can see through clouds. SAR is potentially an all-weather day/night imager. But SAR sensors have limitations in terms of data collection geometry and algorithmic approximations. Both sensors offer complimentary information for exploitation in data fusion for enhanced results. This research is focused on capitalizing the fusion potential for spaceborne High resolution SAR and Optical data in urban settings. The fusion of high reflection of SAR energy from urban areas and optical features of such areas can be combined to enhance the urban infrastructure detection and monitoring in a SAR/Optical fused scenario. SAR/Optical fusion can take place at three levels 1) pixel level, 2) feature level; and 3) information level. Pixel level fusion is often considered most difficult for high resolution data as precise registration up to subpixel level is required and even slight misregistration results in unfavorable circumstances. Simon Fraser University (SFU) Burnaby Mountain Campus has been chosen for area of interest because of its ongoing student housing and university infrastructure developmental projects. TerraSAR-X High Resolution Spotlight (TSX-HS) Single Look Complex (SLC) images of 1.0 m resolution have continuously being acquired over SFU; along with high resolution Optical (RGB) and Infrared (IR) images (3.0 m resolution each) from “The Planet” acquisitions. Limited high-resolution images from “Google Earth” (GE) in the coinciding period of TSX-HS acquisitions were also acquired for the study. Six fusion techniques have been studied for urban infrastructure detection and have been categorized based on their performance. Precision change maps will be created based on time series analysis for SAR/optical fused data in conjunction with Interferometric SAR (InSAR) analysis to study the long-term effect of urban infrastructure developments over a period of two years.

Proceedings Article | 9 May 2012 Paper

Tunnel monitoring with an advanced InSAR technique

Bernhard Rabus, Jayson Eppler, Jayanti Sharma, Jennifer Busler

Proceedings Volume 8361, 83611F (2012) https://doi.org/10.1117/12.918644

KEYWORDS: Interferometric synthetic aperture radar, Digital filtering, Picosecond phenomena, Nonlinear filtering, Linear filtering, Sensors, Data modeling, Prototyping, Temperature metrology, Water

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Proceedings Article | 3 May 2012 Paper

A geometry-based image search engine for advanced RADARSAT-1/2 GIS applications

Vinay Kotamraju, Bernhard Rabus, Jennifer Busler

Proceedings Volume 8396, 839607 (2012) https://doi.org/10.1117/12.920827

KEYWORDS: Synthetic aperture radar, Interferometric synthetic aperture radar, Databases, Geographic information systems, Sensors, Image visualization, Visualization, Human-machine interfaces, Satellites, Image retrieval

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Proceedings Article | 5 May 2009 Paper

Preliminary validation of RADARSAT-2 surface soil moisture estimates

Jan Hendrickx, Bernard Rabus, Diana Romero, Hans Wehn, J. Bruce Harrison, Sung-ho Hong, Brian Borchers

Proceedings Volume 7303, 730310 (2009) https://doi.org/10.1117/12.820046

KEYWORDS: Soil science, Radar, Sensors, Surface roughness, Vegetation, Data modeling, Backscatter, Land mines, Earth observing sensors, Landsat

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