Automatic Target Recognition Using a Modular Neural Network.
Abstract
A modular neural network classifier has been applied to the problem of automatic target recognition (ATR) of targets in 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. The classifier's performance is further improved by the use of multiresolution features and by the introduction of a higher level neural network on top of the 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1998
- Accession Number
- ADA345009
Entities
People
- Lin-cheng Wang
- Nasser M. Nasrabadi
- Sandor Der
Organizations
- United States Army Research Laboratory