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Multiscale Estimation of Terrain Complexity Using ALSM Point Data on Variable Resolution Grids

  • K.C. Slatton
  • K. Nagarajan
  • V. Aggarwal
  • H. Lee
  • W. Carter
  • R. Shrestha
Part of the International Association of Geodesy Symposia book series (IAG SYMPOSIA, volume 129)

Abstract

Multiscale Kalman smoothers (MKS) have been previously employed for data fusion applications and estimation of topography. However, the standard MKS algorithm embedded with a single stochastic model gives suboptimal performance when estimating non-stationary topographic variations, particularly when there are sudden changes in the terrain. In this work, multiple MKS models are regulated by a mixture-of-experts (MOE) network to adaptively fuse the estimates. Though MOE has been widely applied to one-dimensional time series data, its extension to multiscale estimation is new.

Keywords

Fractal Dimension Kalman Filter Fractional Brownian Motion Hurst Exponent Interferometric Synthetic Aperture Radar 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • K.C. Slatton
    • 1
    • 2
  • K. Nagarajan
    • 1
  • V. Aggarwal
    • 1
  • H. Lee
    • 1
  • W. Carter
    • 2
  • R. Shrestha
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of FloridaGainesville
  2. 2.Department of Civil and Coastal EngineeringUniversity of FloridaGainesville

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