Morphological shared-weight neural networks: a tool for automatic target recognition beyond the visible spectrum

Abstract

Morphological shared-weight neural networks (MSNN) combine the feature extraction capability of mathematical morphology with the function mapping capability of neural networks. This provides a trainable mechanism for translation invariant object detection using a variety of imaging sensors, including TV, forward-looking infrared (FLIR) and synthetic aperture radar (SAR). We provide an overview of previous results and new results with laser radar (LADAR). We present three sets of experiments. In the first set of experiments we use the MSNN to detect different types of targets simultaneously. In the second set we use the MSNN to detect only a particular type of target. In the third set we test a novel scenario: we train the MSNN to recognize a particular type of target using very few examples. A detection rate of 86% with a reasonable number of false alarms was achieved in the first set of experiments and a detection rate of close to 100% with very few false alarms was achieved in the second and third sets of experiments.