Continuous-output neural networks for EM ellipticity pattern recognition

Research output: Contribution to conferencePaper

Abstract

The interpretation of images of magnetic field polarization (ellipticity) acquired with a geophysics electromagnetic (EM) imaging system is treated as a pattern-recognition problem. A continuous-output backpropagation network is presented with images from a target in various locations and is taught to associate the spatial location of the target with the pattern in the image. Five different data representations were examined for training speed, accuracy, and generalization capabilities. The results are shown to be relatively insensitive to network design, but the overall errors decrease as the the size of the input vector decreases. The network located the target in the field data within 3% of the horizontal location and 1.2% of the depth.

Original languageEnglish (US)
Pages1297-1300
Number of pages4
StatePublished - Dec 1 1990
Event10th Annual International Geoscience and Remote Sensing Symposium - IGARSS '90 - College Park, MD, USA
Duration: May 20 1990May 20 1990

Other

Other10th Annual International Geoscience and Remote Sensing Symposium - IGARSS '90
CityCollege Park, MD, USA
Period5/20/905/20/90

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Poulton, M. M., Sternberg, B. K., & Glass, C. E. (1990). Continuous-output neural networks for EM ellipticity pattern recognition. 1297-1300. Paper presented at 10th Annual International Geoscience and Remote Sensing Symposium - IGARSS '90, College Park, MD, USA, .