Robustness of features for automatic target discrimination in high resolution polarimetric SAR data

We have studied the robustness of features against aspect variability for the purpose of target discrimination using polarimetric 35 Ghz ISAR data. Images at a resolution of 10 cm and 30 cm have been used for a complete aspect range of 360 degrees. The data covered four military targets: T72, ZSU23/4, T62, and BMP2. For the study we composed several feature vectors out of individual features extracted from the images. The features are divided into three categories: radiometric, geometric and polarimetric. We found that individual features show a strong variability as a function of aspect angle and cannot be used to discriminate between the targets irrespectively of the aspect angle. Using feature vectors and a maximum likelihood classifier reasonable discrimination (about 80%) between the four targets irrespective of the aspect angle was obtained at 10 cm resolution. At 30 cm resolution less significant discrimination (less than 70%) was found irrespective of the kind of feature vector used. In addition we investigated target discrimination per 30-degree aspect interval. In order to determine the aspect angle of targets we used a technique based on the Radon transformation, which gave an accuracy of about 5 degrees in aspect angle. We found that in this case good discrimination (more than 90%) was obtained at 10 cm resolution and reasonable discrimination (about 80%) at 30 cm resolution. The results are compared with analogous results from MSTAR data (30 cm resolution) of comparable targets.