Sensor feature fusion for detecting buried objects

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Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a ... continued below

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13 p.

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Clark, G. A.; Sengupta, S. K.; Sherwood, R. J.; Hernandez, J. E.; Buhl, M. R.; Schaich, P. C. et al. April 1, 1993.

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Description

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. 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. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. 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 sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.

Physical Description

13 p.

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OSTI; NTIS; GPO Dep.

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  • Society of Photo-Optical Instrumentation Engineers (SPIE) OE/aerospace science and sensing meeting,Orlando, FL (United States),11-16 Apr 1993

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  • Other: DE93018666
  • Report No.: UCRL-JC--113727
  • Report No.: CONF-930445--22
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 10180523
  • Archival Resource Key: ark:/67531/metadc1401142

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  • April 1, 1993

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  • Jan. 12, 2019, 4:41 p.m.

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  • March 8, 2019, 12:22 p.m.

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Clark, G. A.; Sengupta, S. K.; Sherwood, R. J.; Hernandez, J. E.; Buhl, M. R.; Schaich, P. C. et al. Sensor feature fusion for detecting buried objects, article, April 1, 1993; California. (https://digital.library.unt.edu/ark:/67531/metadc1401142/: accessed March 21, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.