The Multiplicative Zak Transform, Dimension Reduction, and Wavelet Analysis of LIDAR Data

Abstract

This thesis broadly introduces several techniques within the context of timescale analysis. The representation, compression and reconstruction of DEM and LIDAR data types is studied with directional wavelet methods and the wedgelet decomposition. The optimality of the contourlet transform, and then the wedgelet transform is evaluated with a valuable new structural similarity index. Dimension reduction for material classification is conducted with a frame-based kernel pipeline and a spectral-spatial method using wavelet packets. It is shown that these techniques can improve on baseline material classification methods while significantly reducing the amount of data. Finally, the multiplicative Zak transform is modified to allow the study and partial characterization of wavelet frames.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA630863

Entities

People

  • Justin C. Flake

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Data Mining
  • Dimensionality Reduction
  • Electrical Engineering
  • Engineering
  • Hyperspectral Imagery
  • Image Processing
  • Information Theory
  • Machine Learning
  • Materials
  • Mathematics
  • Mobile Phones
  • Signal Processing
  • Theorems
  • Theses
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.