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Remote sensing operations

F Badran1, C Mejia, S Thiria

  • 1CEDRIC, Conservatoire National des Arts et Métiers, Paris, France.

International Journal of Neural Systems
|December 1, 1995
PubMed
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Neural Networks effectively model complex multivalued transfer functions. This study introduces a conditional density approximation method validated using remote sensing data.

Area of Science:

  • Machine Learning
  • Remote Sensing
  • Signal Processing

Background:

  • Multivalued transfer functions are common in complex systems.
  • Modeling these functions accurately is challenging.
  • Existing methods may lack efficiency or scalability.

Purpose of the Study:

  • To demonstrate the efficacy of Neural Networks in modeling multivalued transfer functions.
  • To introduce a novel conditional density approximation approach.
  • To validate the proposed method in a practical remote sensing application.

Main Methods:

  • Utilized Neural Networks for function approximation.
  • Developed a method based on conditional density estimation, p(y|x).
  • Applied and tested the approach on a remote sensing dataset.

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Main Results:

  • Neural Networks demonstrated efficient modeling of multivalued transfer functions.
  • The conditional density approximation method proved valid and effective.
  • Successful application in a real-world remote sensing scenario.

Conclusions:

  • Neural Networks offer a powerful tool for modeling complex multivalued functions.
  • The proposed conditional density approximation method is a viable approach.
  • This technique has significant potential for remote sensing and related fields.