Classification of targets by polarimetric information

本课题的研究目的是利用电磁信息的全矢量特性对雷达目标进行分类。基于Sinclair散射矩阵的雷达极化测量原理使我们能够根据以下各项对目标进行分类：特征极化状态极化增强将散射矩阵分解为三种基本类型：球形、双平面、螺旋形极化各向异性系数极化熵利用上述各项对目标进行信息检索。本文还提出了一种波纹板波导作为偏振校准器。该校准器具有直线偏振特性，工作在超宽带宽度。对雷达系统进行极化定标后，得到了精确的散射矩阵，并将其应用于实验室目标分类测量。将散射矩阵分解为3个基本分量，得到了比较满意的结果，验证了极化信息在雷达遥感中的重要性。该系统还被应用于地下物体的检测。利用极化各向异性系数和特征极化状态对地下目标进行了分类。针对NASA-JPL AIRSAR、Shuttle Imaging Radar-C/X-SAR和CRL/NASDA AIRSAR的合成孔径雷达（SAR）图像分析，引入了一种新的极化信息，极化熵基于最大似然法的熵与散射矩阵元素、小波变换相结合，提供了高水平的分类性能。

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 statePolarimetric enhancementDecomposition of scattering matrix into three basic types, sphere, diplane, helix.Polarimetric anisotropy coefficientPolarimetric entropyUsing 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.