Abstract
We investigate the potential benefits of fusing two bands of forward-looking infrared (FLIR) data for target detection and clutter rejection. We propose a similar set of neural-based clutter rejecters and target detectors, each of which consists of an eigenspace transformation and a simple multilayer perceptron. The same architecture is used to operate on either single band or dualband FLIR input images, so that the net effects of dualband fusion can be demonstrated. When the dualband inputs are used, the component bands are combined at either pixel or feature level, thus providing insight into methods of performing data fusion in this particular application. A large set of real FLIR images is used in two series of experiments, one for clutter rejection tasks and the other for target detection tasks. In both series, the results indicate that the dualband input images do improve the performance of the clutter rejecters and target detectors over their single band counterparts. On the other hand, results of the pixel and feature level fusions are quite similar, suggesting that dimensionality reduction by the eigenspace transformation can be performed independently on the two bands.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call