Mr. John G. Sutherland Profile

John G. Sutherland

President at AND Corp

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Proceedings Article | 27 April 2007 Paper

Land mine detection applying holographic neural technology (HNeT)

John Sutherland, William Radzelovage

Proceedings Volume 6553, 655323 (2007) https://doi.org/10.1117/12.729264

KEYWORDS: Land mines, Metals, Sensors, Target detection, Palladium, Synthetic aperture radar, Mining, Image sensors, Holography, Stereolithography

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Provided is a summary of Holographic Neural Technology (HNeT) and its application in detecting land mines using airborne Synthetic Aperture Radar (SAR) imagery. Tests were performed for three surface mine classes (small metallic, large metallic, and medium-sized plastic) located within variable indigenous background clutter (bare dirt, short/tall grass). This work has been performed as part of the Wide Area Airborne Minefield Detection (WAAMD) Program at the U. S. Army Night Vision Labs and Electronic Sensors Directorate in Fort Belvoir, VA. The ATR algorithm applied was Holographic Neural Technology (HNeT); a neuromorphic model based upon non-linear phase coherence/de-coherence principles. The HNeT technology provides rapid learning capabilities and an advanced capability in learning and generalization of non-linear relationships. Described is a summary of the underlying HNeT technology and the methodologies applied in the training of the neuromorphic system for mine detection using target images (land mines) and back ground clutter images. Provided also is a summary description of the software tools applied in the development of the mine detection capability. Performance testing of the mine detection algorithm separated training and testing sensor image sets by airborne sensor depression angle and surface ground condition indigenous to site location (Countermine Alpha, Yellow Sands). Detection performance was compared in the analysis of complex versus magnitude sensor data. Performance results from independent test imagery indicated a reasonable level of clutter rejection, providing > 50% probability of detection at a false detection rate < 10^-3/m². A description of the test scenarios applied and performance results for these scenarios are summarized in this report.

Proceedings Article | 22 October 2001 Paper

Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

Paul Pace, John Sutherland

Proceedings Volume 4379, (2001) https://doi.org/10.1117/12.445381

KEYWORDS: Target detection, Biomimetics, Holography, Target recognition, Sensors, Automatic target recognition, Image analysis, Environmental sensing, Intelligence systems, Facial recognition systems

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This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

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