Clutter rejection using eigenspace transformation

An effective clutter rejection scheme is needed to distinguish between clutter and targets in a high-performance automatic target recognition system. In this paper, we present a clutter rejection scheme that consists of an eigenspace transformation and a multilayer perceptron (MLP). The input to the clutter rejector module is the output of the detector that provides the potential regions (target chips). We first use an eigen transformation for feature extraction and dimensionality reduction. The transformations considered in this research are principal component analysis (PCA) and the eigenspace separation transform (EST). These transformations differ in their capabilities to enhance the class separability and to compact the information (energy) for a given training set. The result of the eigenspace transformation is then fed to an MLP that predicts the identity of the input, which is either a target or clutter. To search for the optimal performance, we use different sets of eigentargets and construct the matching MLPs. Modified from the popular Qprop algorithm, 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 values. Experimental results are presented on a huge and realistic data set of forward-looking infrared (FLIR) imagery.