The performance of several combinations of feature extraction and target classification algorithms is analyzed for Synthetic Aperture Radar (SAR) imagery using the standard Moving and Stationary Target Acquisition and Recognition (MSTAR) evaluation method. For feature extraction, 2D Fast Fourier Transform (FFT) is used to extract Fourier coefficients (frequency information) while 2D wavelet decomposition is used to extract wavelet coefficients (time-frequency information), from which subsets of characteristic in-class "invariant" coefficients are developed. Confusion matrices and Receiver Operating Characteristic (ROC) curves are used to evaluate and compare combinations of these characteristic coefficients with several classification methods, including Lp metric distances, a Multi Layer Perceptron (MLP) Neural Network (NN) and AND Corporation's Holographic Neural Technology (HNeT) classifier. The evaluation method examines the trade-off between correct detection rate and false alarm rate for each combination of feature-classifier systems. It also measures correct classification, misclassification and rejection rates for a 90% detection rate. Our analysis demonstrates the importance of feature and classifier selection in accurately classifying new target images.
The ATR Workbench is an evaluation platform implemented to assist in
the development of automation techniques for target recognition within SAR imagery. This will allow researchers and Image Analysts (IAs) to investigate the capabilities of various commercial and experimental applications, singly or in combination, as applied to the target recognition process. The platform will enable studies to determine which aspects of the target recognition process improve IA performance when automated, which methods best improve classifier performance, as well as which methods work better for particular environments and target class definitions. Based largely on open-source tools, the Workbench was developed so as to provide a platform independent bridge between automatic target detection (ATD) applications and target classifiers. It is capable of importing several kinds of ATD reports, of applying different feature extraction and preprocessing algorithms and of implementing various aspects of automatic target recognition (ATR) applications while importing, displaying and reporting their results. Each step may be automated or operated interactively, as required. Initially, this capability is demonstrated on imagery based upon the public MSTAR data set.