Selectively optimized networks for automatic clutter rejection
An effective clutter rejection scheme is needed to distinguish between clutter and targets in a high-performance automatic target recognition (ATR) system. We present a clutter rejection scheme that consists of an eigenspace transformation and a multilayer perceptron (MLP). We use either principal component analysis (PCA) or the eigenspace separation transform (EST) to perform feature extraction and dimensionality reduction. The transformed data is then fed to an MLP that predicts the identity of the input, which is either a target or clutter. We devise an MLP training algorithm that seeks to maximize the class separation at a given false-alarm rate, which does not necessarily minimize the average deviation of the MLP outputs from their target valves. Experimental results are presented on a huge and realistic dataset of forward-looking infrared imagery.