Remote sensing of rough surface parameters using artificial neural network technique

Akira Ishimaru, Jenq Neng Hwang, Kuniaki Yoshitomi, Jei Shuan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The artificial neural network (ANN) technique is applied to the remote sensing of the rms height and the correlation distance of one-dimensional rough surfaces. The surface is illuminated by a beam wave, and the intensity correlations of the scattered wave at two wavelengths in the specular and backward directions are used to determine the roughness parameters. Scattered intensity correlations calculated by Monte Carlo simulations are used to train the ANN, and two methods, the explicit inversion method and the iterative constrained inversion method, are used to perform the inversion. The technique is applicable to the range of parameters, 0.2 <σ/λ <1.0 and 1.0 < ℓ/λ < 5.0, where σ is the rms height and ℓ is the correlation distance of the surface roughness. An optimum surface area illuminated by the incident beam is approximately 20λ. Both the explicit inverse method and the iterative constrained inversion method give inversion values which are close to the target values. The iterative constrained inversion method appears to give smaller errors, although the required computer time is longer.

Original languageEnglish
Title of host publicationIGARSS 1992 - International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Space Year: Space Remote Sensing
EditorsRuby Williamson, Tammy Stein
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1072-1074
Number of pages3
ISBN (Electronic)0780301382
DOIs
Publication statusPublished - Jan 1 1992
Event12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States
Duration: May 26 1992May 29 1992

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2

Other

Other12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992
CountryUnited States
CityHouston
Period5/26/925/29/92

All Science Journal Classification (ASJC) codes

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

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