Target recognition for FLIR imagery using learning vector quantization and multilayer perceptrons

Vincent Mirelli; Duc Minh Nguyen; Nasser M. Nasrabadi

doi:10.1117/12.213075

5 July 1995 Target recognition for FLIR imagery using learning vector quantization and multilayer perceptrons

Vincent Mirelli, Duc Minh Nguyen, Nasser M. Nasrabadi

Proceedings Volume 2485, Automatic Object Recognition V; (1995) https://doi.org/10.1117/12.213075
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

In this paper a neural-network-based automatic target recognition (ATR) classifier is developed. The ATR classifier consists of a learning vector quantization (LVQ) algorithm followed by a multilayer perceptron (MLP). The LVQ is used as the feature extractor and the MLP as the classifier. The LVQ algorithm adaptively extracts a set of target templates (centroids) that are assumed to represent the target signatures. The Euclidean distances between the centroids and the input target are passed to an MLP. The MLP uses these distances as input and performs a classification. Experimental results are presented for two different test sets. The first test set has similar characteristics to those of the training set, and the ATR classifier does very well. However, the second test set has a different characteristics and the ATR classifier performance is poor.

Citation Download Citation

Vincent Mirelli, Duc Minh Nguyen, and Nasser M. Nasrabadi "Target recognition for FLIR imagery using learning vector quantization and multilayer perceptrons", Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); https://doi.org/10.1117/12.213075

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