Mr. Thomas P. Donzelli Profile

Thomas P. Donzelli

at US Army CCDC C5ISR Ctr Night Vision & Electronic Sensors Directorate

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Publications (4)

Proceedings Article | 15 May 2015 Paper

A synthetic aperture acoustic prototype system

Robert Luke, Steven Bishop, Aaron Chan, Peter Gugino, Thomas Donzelli, Mehrdad Soumekh

Proceedings Volume 9454, 94540Y (2015) https://doi.org/10.1117/12.2177457

KEYWORDS: Acoustics, Transceivers, Foam, Prototyping, Imaging systems, Signal processing, Interference (communication), Target detection, General packet radio service, Signal to noise ratio

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Proceedings Article | 11 September 2003 Paper

Airborne change detection system for the detection of route mines

Thomas Donzelli, Larry Jackson, Mark Yeshnik, Thomas Petty

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.486943

KEYWORDS: Roads, Video, Sensors, Mining, Land mines, Image processing, Computing systems, Infrared sensors, Mid-IR, Image segmentation

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Proceedings Article | 18 October 2001 Paper

Surface mine detection in the 3- to 5-um band using neural networks

Thomas Donzelli, Hanna Haskett

Proceedings Volume 4394, (2001) https://doi.org/10.1117/12.445483

KEYWORDS: Mining, Neural networks, Land mines, Neurons, Image processing, Palladium, Mid-IR, Sensors, Target detection, Cameras

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Proceedings Article | 18 October 2001 Paper

Minefield performance for LAMD using AMBER broadband/multiband MWIR imagery

Hanna Haskett, Thomas Donzelli

Proceedings Volume 4394, (2001) https://doi.org/10.1117/12.445478

KEYWORDS: Land mines, Mining, Detection and tracking algorithms, Target detection, Sensors, Algorithm development, Mid-IR, Palladium, Automatic target recognition, Statistical analysis

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This paper includes analysis/assessment and development of detection algorithms: (1) the assessment of detectability of surface mines using the RX algorithm implementation, which in turn, provides a first look at the limitation of the algorithm for suitable real-time implementation; (2) the development of the adaptive real-time mine detection algorithm (ARMD) based on statistical analysis of the data. The statistical analysis includes the class distribution between mines and background, the underlying distribution for mines and background based on the quantile-quantile plot. The paper also compares the quantitative performance of probability of detection (Pd) and false alarm rates (FAR) for different detection techniques for different background and mine types. This paper also presents the minefield probability of detection versus minefield false alarm rate to gauge the minefield detection performance trade-off using: (1) only mine density; and (2) mine density with pattern. This paper also demonstrates the importance of the observables that offer the class separability between mine/target and background for automatic target detection/recognition applications. Detection algorithms with high computational capability are not the 'silver bullet' for automatic target detection/recognition as commonly believed. The art of ATR is the ability to be able to pinpoint the observables that distinguish mines and background. Once the observables offer the class separability between classes are established, any simple correlation method can deliver an acceptable performance (demonstrating that highly computational methods, indeed, are wasteful and unnecessary). This paper uses the multiband and broadband data collected with the AMBER (3.5-5)mum) camera in May 2000. This data set contains about 513 (approximately 1.1 in resolution) images covering three spectral regions: 3-5)mum, 3- 4.2)mua and 4.2-5)mua. The total number of mines and the area coverage for these three spectral regions are approximately 579, and 25200m², respectively. Note that each spectral region contains 171 images (of which 53 images contain mines with 131 large mines, and 62 small mines) covering about 8400m². Also note that to stimulate minefields, an image containing 3 mines with a straight line pattern is defined as a minefield opportunity.

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