Dr. John C. Hyland Profile

Dr. John C. Hyland

at Naval Surface Warfare Ctr

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Author

Publications (4)

Proceedings Article | 21 May 2015 Paper

Automated area segmentation for ocean bottom surveys

John Hyland, Cheryl Smith

Proceedings Volume 9454, 94541N (2015) https://doi.org/10.1117/12.2179777

KEYWORDS: Image segmentation, Sensors, Sensor performance, Algorithm development, Palladium, Environmental sensing, Composites, Land mines, Signal to noise ratio, Warfare

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In practice, environmental information about an ocean bottom area to be searched using SONAR is often known a priori to some coarse level of resolution. The SONAR search sensor then typically has a different performance characterization function for each environmental classification. Large ocean bottom surveys using search SONAR can pose some difficulties when the environmental conditions vary significantly over the search area because search planning tools cannot adequately segment the area into sub-regions of homogeneous search sensor performance. Such segmentation is critically important to unmanned search vehicles; homogenous bottom segmentation will result in more accurate predictions of search performance and area coverage rate. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed an automated area segmentation algorithm that subdivides the mission area under the constraint that the variation of the search sensor’s performance within each sub-mission area cannot exceed a specified threshold, thereby creating sub-regions of homogeneous sensor performance. The algorithm also calculates a new, composite sensor performance function for each sub-mission area. The technique accounts for practical constraints such as enforcing a minimum sub-mission area size and requiring sub-mission areas to be rectangular. Segmentation occurs both across the rows and down the columns of the mission area. Ideally, mission planning should consider both segmentation directions and choose the one with the more favorable result. The Automated Area Segmentation Algorithm was tested using two a priori bottom segmentations: rectangular and triangular; and two search sensor configurations: a set of three bi-modal curves and a set of three uni-modal curves. For each of these four scenarios, the Automated Area Segmentation Algorithm automatically partitioned the mission area across rows and down columns to create regions with homogeneous sensor performance. The testing results indicated that the algorithm correctly segmented the rectangular a priori regions. For the triangular a priori segmentation, the algorithm created reasonable rectangular sub-areas.

Proceedings Article | 21 September 2004 Paper

Optimal reload strategies for identify-and-destroy missions

John Hyland, Cheryl Smith

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.562760

KEYWORDS: Mining, Detection and tracking algorithms, Sensors, Target detection, Computer programming, Land mines, Algorithm development, Diamond, Target recognition, 3D acquisition

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Proceedings Article | 22 July 1997 Paper

Automated detection and classification of sea mines in sonar imagery

Gerald Dobeck, John Hyland, Le'Derick Smedley

Proceedings Volume 3079, (1997) https://doi.org/10.1117/12.280846

KEYWORDS: Mining, Image classification, Nonlinear filtering, Detection and tracking algorithms, Target detection, Algorithm development, Land mines, Neural networks, Feature extraction, Feature selection

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Proceedings Article | 20 June 1995 Paper

Sea mine detection and classification using side-looking sonar

John Hyland, Gerald Dobeck

Proceedings Volume 2496, (1995) https://doi.org/10.1117/12.211341

KEYWORDS: Mining, Land mines, Neural networks, Image filtering, Target detection, Image processing, Target recognition, Image classification, Naval mines, Classification systems

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