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Light Aircraft Synthetic Aperture Radar (SAR) for High Resolution (1.2-meter) Topographic Mapping & Land Type Classification. Dennis B. TRIZNA, Ph. D. Research Professor CEOSR - GMU e-mail: triznad@erols.com. OUTLINE OF PRESENTATION. Background NUWSAR Multi-band Polarimetric SAR
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Light Aircraft Synthetic Aperture Radar (SAR) for High Resolution (1.2-meter) Topographic Mapping & Land Type Classification Dennis B. TRIZNA, Ph. D. Research Professor CEOSR - GMU e-mail: triznad@erols.com
OUTLINE OF PRESENTATION • Background • NUWSAR Multi-band Polarimetric SAR • Objectives • Experimental Data Characterization • Projection Pursuit Analysis Tool • Analysis Results • Summary
BACKGROUND: • NUWSAR Light Aircraft SAR developed • Pulse-to-pulse polarization, frequency band switching • L and X band are first bands chosen for implementation • Full Motion Compensation (Litton 100G & DGPS) • Polarization/Frequency suite offers opportunity for Classification tool application - 6 Channels simultaneous • Projection Pursuit developed for multiple image data • Higher dimension data set tool • Uses local texture in scene as analysis component
NUWSAR - Naval UltraWideband Synthetic Aperture Radar Navy SBIR - Local Fairfax Company, Warrenton Air Operations Piper Navaho Platform L-band 45-deg Beam Width Horn 3 X-band 11-deg Beam Width Horns for AT & CT INSAR Litton 100G IMU
Part I - Land Classification • Method • Multiple radar Frequencies & Polarizations • Simultaneous operation for registration, comparison • Projection Pursuit Classification methods, GIS • Applications: • Forest, Crop Health • Land Useage • Change detection over months/years • Watershed, wetlands • Forest cover, cropland percentage • Developed areas • Roads, Highways
X-band 3-Pol HH/HV/VV=RGB Nulls of X-band Horn
Projection Pursuit Analysis of 6-channel images in a variety of combinations • A Generalized approach to Automatic Target Detection, Classification, and Identification • Has been applied to ASARS radar data - single frequency • Identification capability developed for targets, other features. • New multi-parameter (Frequency & Polarization) capability enhances ability to identify & classify.
Projection Pursuit Classification method -applied to study of 6 channels of 1.2-m data Imagery: Lhh, Lhv, Lvv, Xhh, Xhv, Xhh. • Develop a PP data projection for this set of 6 Frequency/Polarization combinations. • Probability field determined from training set on portion of data projection image. • Develop Normalized Mutual Information function for quantitative comparison of different channel combinations for each of different target or scene types.
Automatic Classification Green: Trees Blue: Tilled Field Red: Grass Yellow: Corn Stubble Purple: Buildings Black: Asphalt Light Blue: Hayfield White: Unknown/Unlabelled Human Interpretation
Part II - Topographic Mapping of Land and Tree Height • Method • Two antennas one above the other used as interferometer • Phase difference of pair relates to height variation • X, L, P - bands map different depths (tree tops, tree branches, ground) • Applications: • Tree height monitoring • Crop height • Building development
Flight direction Run 09119957 - Warrenton-Fauquier Airport Flight direction Map of the area Imaged portion of the area
Area map and its SAR image Map SAR image
No elevation data available because of poor SNR (shadows, etc) m Relative elevations from InSAR SARImage Elevation extraction from InSAR
m 3-D Elevation Plot from Interferometric SAR Resolution of the elevation plot was reduced to 24 m x 24 m Look
3-D Elevation Plot - 2nd Example-Quarry (Incomplete Analysis) SAR image Map
Look Picture of the part of the area
m No elevation data available because of poor SNR (shadows, etc) or phase wrapping problems Elevations above geoid from InSAR Resolution was reduced to 6 m x 6 m to achieve better statistical averaging SAR Image
m 3-D Elevation Plot from Interferometric SAR Resolution of the elevation plot was reduced to 6 m x 6 m Look
Potential State Use of Light Aircraft High Resolution Data: • Land Classification: • Tree & Crop Health • Crop height • Land Use - Building development, Percentage Forest/Wetlands/Development • Topographic Mapping Data: • Forest Management • Land use - change detection over months/years • Coastline migration • Watershed changes-Couple with Airborne Hyperspectral Classification