Automatic target recognition using vector quantization and neural networks

We propose an automatic target recognition (ATR) algorithm that uses a set of dedicated vector quantizers (VQs) and multilayer perceptrons (MLPs). For each target class at a specific range of aspects, the background pixels of an input image are first removed. The extracted target area is then subdivided into several subimages. A dedicated VQ codebook is constructed for each of the resulting subimages. Using the K-means algorithm, each VQ codebook learns a set of patterns representing the local features of a particular target for a specific range of aspects. The resulting codebooks are further trained by a modified learning vector quantization (LVQ) algorithm, which enhances the discriminatory power of the codebooks. Each final codebook is expected to give the lowest mean squared error (MSE) for its correct target class and range of aspects. These MSEs are then input to an array of window-level MLPs (WMLPs), where each WMLP is specialized in recognizing its intended target class for a specific range of aspects. The outputs of these WMLPs are manipulated and passed to a target-level MLP, which produces the final recognition results. We trained and tested the proposed ATR algorithm on large and realistic data sets and obtained impressive results using the wavelet-based adaptive product VQs configuration. (C) 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99)02712-9].