The Use of Artificial Neural Networks for Automatic Target Recognition
Nasser M. Nasrabadi, Sandor Z. Der, Lincheng Wang, Syed A. Rizvi, Alex Lipchen Chan · Studies in fuzziness and soft computing · 2000
A number of learning algorithm based automatic target recognizers have been developed. The approaches differ in how features for recognition are extracted and in the architecture of the recognizer. Features are either extracted automatically by a multilayer convolutional neural network, or chosen by the designer based on experiment and previous experience. Recognizer complexity is kept low by decomposing the learning tasks using modular components or imposing an architecture that is not fully connected. Three complete recognizers have been developed, and we label them as modular neural network (MNN), learning vector quantization (LVQ), and convolutional neural network (CNN). MNN uses modular neural networks operating on local directional variances of the image. LVQ uses the Haar wavelet decomposition of the input images as features, clusters training features into templates using the K-means algorithm, and then enhances the recognition capability of the templates using learning vector quantization. CNN operates directly on input images without any preliminary feature extraction stage. The multilayer convolutional neural network simultaneously learns features and how to classify them. The performance of the recognizers are compared by probability of recognition and computational complexity.