| Research Abstract |
The purpose of the research project is to classify radar targets using full vector nature of electromagnetic information. The principle of radar polarimetry based on Sinclair scattering matrix enables us to classify targets by the following items :
Characteristic polarization state
Polarimetric enhancement
Decomposition of scattering matrix into three basic types, sphere, diplane, helix.
Polarimetric anisotropy coefficient
Polarimetric entropy
Using the above items, we tried to retrieve information on target.
At first, we have designed and developed a full polarimetric FM-CW radar system operative in real-time mode in the X-band, which acquires 44 scattering matrices per second. A corrugated plate waveguides is also proposed as a polarimetric calibrator. The calibrator has a straight line polarization characteristic and operates in ultra wideband width. After the polarimetric calibration of the radar system, it yielded accurate scattering matrices.
Then, the radar system was applied to classification of targets in a laboratory measurement. The decomposition of scattering matrix into 3 basic components provided quite satisfactory results, and it verified the importance of polarimetric information in radar remote sensing. The system has been also applied to the detection of objects in the underground. Using polarization anisotropy coefficient together with characteristic polarization states, the radar system yielded a fine classification results of buried target.
For Synthetic Aperture Radar (SAR) image analysis, acquired with NASA-JPL AIRSAR, Shuttle Imaging Radar-C/X-SAR, and CRL/NASDA AIRSAR, we introduced an additional polarimetric information, i.e., polarimetric entropy. The combination of entropy with scattering matrix element, wavelet transform, based on the maximum likelihood method provided high-level classification performance.
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