LADAR target detection using morphological shared-weight neural networks

Abstract. Morphological shared-weight neural networks (MSNN) combine the feature extraction capability of mathematical morphology with the function-mapping capability of neural networks in a single trainable architecture. The MSNN method has been previously demonstrated using a variety of imaging sensors, including TV, forward-looking infrared (FLIR) and synthetic aperture radar (SAR). In this paper, we provide experimental 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, referred to as the Sims 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. In all the experiments, a novel pre-processing method is used to create a pseudo-intensity images from the original LADAR range images.

[1]  Paul D. Gader,et al.  Entropy optimized morphological shared-weight neural networks , 1999 .

[2]  Abhijit Mahalanobis,et al.  Synthetic discriminant functions using relaxed constraints , 1993, Defense, Security, and Sensing.

[3]  Paul D. Gader,et al.  Nonlinear correlation filter and morphology neural networks for image pattern and automatic target recognition , 1995 .

[4]  B. Bhanu,et al.  Image understanding research for automatic target recognition , 1993, IEEE Aerospace and Electronic Systems Magazine.

[5]  Paul Sajda,et al.  Integrating neural networks with image pyramids to learn target context , 1995, Neural Networks.

[6]  Paul D. Gader,et al.  Detection and classification of MSTAR objects via morphological shared-weight neural networks , 1998, Defense, Security, and Sensing.

[7]  M. W. Roth.  Survey of neural network technology for automatic target recognition , 1990, IEEE Trans. Neural Networks.

[8]  Paul D. Gader,et al.  Morphological shared-weight networks with applications to automatic target recognition , 1997, IEEE Trans. Neural Networks.

[9]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[10]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[11]  D. Casasent,et al.  Correlation synthetic discriminant functions. , 1986, Applied optics.

[12]  Paul D. Gader,et al.  Minimum average correlation energy (MACE) prefilter networks for automatic target recognition , 1994, Defense, Security, and Sensing.

[13]  PAUL D. GADER,et al.  Segmentation free shared weight networks for automatic vehicle detection , 1995, Neural Networks.

[14]  Paul D. Gader,et al.  Image algebra networks for pattern classification , 1994, Optics & Photonics.

[15]  Jian Li,et al.  Target detection with synthetic aperture radar , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[16]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[17]  Andre J. Oosterlinck,et al.  Architecture and algorithms for digital image processing : August 25-26, 1983, San Diego, California , 1983 .

[18]  D Casasent,et al.  Advanced distortion-invariant minimum average correlation energy (MACE) filters. , 1992, Applied optics.

[19]  M. M. Moya,et al.  Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition , 1995, Neural Networks.

[20]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  H. Frigui,et al.  A fuzzy logic automatic target detection system for LADAR range images , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).