Morphological neural networks of background clutter adaptive prediction for detection of small targets in image data

Authors:
Honggang Wu;Xiaofeng Li;Zaiming Li;Yuebin Chen
Affiliations:
School of Communication and Information Engineering, University of Electronics Science and Technology of China, Chengdu, China;School of Communication and Information Engineering, University of Electronics Science and Technology of China, Chengdu, China;School of Communication and Information Engineering, University of Electronics Science and Technology of China, Chengdu, China;School of Communication and Information Engineering, University of Electronics Science and Technology of China, Chengdu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 2
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Abstract

An effective morphological neural network of background clutter prediction for detecting small targets in image data is proposed in this paper. The target of interest is assumed to have a very small spatial spread, and is obscured by heavy background clutter. The clutter is predicted exactly by morphological neural networks and subtracted from the input signal, leaving components of the target signal in the residual noise. Computer simulations of real infrared data show better performance compared with other traditional methods.