Analysis of Features for Synthetic Aperture Radar Target Classification.

摘要

Considering two classes of vehicles, we aim to identify the physical elements of the vehicles with the most impact on identifying the class of the vehicle in synthetic aperture radar (SAR) images. We classify vehicles using features, from polarimetric SAR images, corresponding to the structure of physical elements. We demonstrate a method which determines the most impactful features to classification by applying subset selection on the features. Determination of the most impactful elements of the vehicles is beneficial to the development of low observables, target models, and automatic target recognition (ATR) algorithms. We show how previous work with features from individual pixels is applied to a greater number of target states. At a greater number of target states, the previous work has poor classification performance. Additionally, the nature of the features from pixels limits the identification of the most impactful elements of vehicles. We apply concepts from optical sensing to reduce the limitation on identification of physical elements. We draw from optical sensing feature extraction with the use of Histogram of Oriented Gradients (HOG). From the cells of HOG, we form features from frequency and polarization attributes of SAR images. Using a subset set of features, we achieve a classification performance of 96.10 percent correct classification. Using the features from HOG and the cells, we identify the features with the most impact. Using backward selection, a process for subset selection, we identify the features with the most impact to classification. The execution of backward selection removes the features which induce the most error