Land mine detection using multispectral image fusion

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Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety ... continued below

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12 p.

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Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.; Roeske, F.; Donetti, J.G.; Fields, D.J. et al. March 29, 1995.

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Description

Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts. We use a supervised learning pattern recognition approach to detecting the metal and plastic land mines. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the spectral bands add value to the detection system. The most important features from the various sensors are fused using a supervised learning pattern classifier (the probabilistic neural network). We present results of experiments to detect land mines from real data collected from an airborne platform, and evaluate the usefulness of fusing feature information from multiple spectral bands.

Physical Description

12 p.

Notes

OSTI as DE95017825

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  • Symposium on autonomous vehicles in mine countermeasures, Monterey, CA (United States), 3-7 Apr 1995

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  • Other: DE95017825
  • Report No.: UCRL-JC--120710
  • Report No.: CONF-9504154--4
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 108165
  • Archival Resource Key: ark:/67531/metadc618992

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 29, 1995

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  • June 16, 2015, 7:43 a.m.

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  • Feb. 23, 2016, 1:47 p.m.

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Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.; Roeske, F.; Donetti, J.G.; Fields, D.J. et al. Land mine detection using multispectral image fusion, article, March 29, 1995; California. (digital.library.unt.edu/ark:/67531/metadc618992/: accessed November 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.