Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

Lin-Cheng Wang; Sandor Z. Der; Nasser M. Nasrabadi

doi:10.1117/12.304646

1 April 1998 Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

Lin-Cheng Wang, Sandor Z. Der, Nasser M. Nasrabadi

Proceedings Volume 3307, Applications of Artificial Neural Networks in Image Processing III; (1998) https://doi.org/10.1117/12.304646
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

A modular neural network classifier has been applied to the problem of automatic target recognition using forward- looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks operating on features extracted from a local portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multi-resolution features and by the introduction of a higher level neural network on the top of expert networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of FLIR images.

Citation Download Citation

Lin-Cheng Wang, Sandor Z. Der, and Nasser M. Nasrabadi "Automatic target recognition using a feature-decomposition and data-decomposition modular neural network", Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); https://doi.org/10.1117/12.304646

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