Using Glint to Perform Geometric Signature Prediction and Pose Estimation

We consider two problems in this paper. The first problem is to construct a dictionary of elements without using synthetic data or a subset of the data collection; the second problem is to estimate the orientation of the vehicle, independent of the elevation angle. These problems are important to the SAR community because it will alleviate the cost to create the dictionary and reduce the number of elements in the dictionary needed for classification. In order to accomplish these tasks, we utilize the glint phenomenology, which is usually viewed as a hindrance in most algorithms but is valuable information in our research. One way to capitalize on the glint information is to predict the location of the flint by using geometry of the single and double bounce phenomenology. After qualitative examination of the results, we were able to deduce that the geometry information was sufficient for accurately predicting the location of the glint. Another way that we exploited the glint characteristics was by using it to extract the angle feature which we will use to do the pose estimation. Using this technique we were able to predict the cardinal heading of the vehicle within +/- 2 degrees with 96.6% having 0 degrees error. Now this research will have an impact on the classification of SAR images because the geometric prediction will reduce the cost and time to develop and maintain the database for SAR ATR systems and the pose estimation will reduce the computational time and improve accuracy of vehicle classification.