Hyperspectral Imagery and LiDAR for Geological Analysis of Cuprite, Nevada

Abstract

Fusion of Light Detection and Ranging (LiDAR) and Hyperspectral Imagery (HSI) products is useful for geological analysis, particularly for visualization of geomorphology and hydrology. In early 2007, coincident hyperspectral imagery and LiDAR were acquired over Cuprite, Nevada. The data were analyzed with ENVI and the ENVI LiDAR Toolkit. Results of the analysis of these data suggest, for some surfaces, a correlation between mineral content and surface roughness. However, the LiDAR resolution (tilde1 meter ground sampling distance) is likely too coarse to extract surface texture properties of clay minerals in some of the alluvial fans captured in the imagery. Though not demonstrated in this particular experiment (but a goal of the research), the relation between surface roughness and mineral composition may provide valuable information about the mechanical properties of the surface coverin addition to generating another variable useful for material characterization, image classification, and scene segmentation. Future mission planning should include consideration of determining optimal ground sampling to be used by LiDAR and HSI systems. The fusion of LiDAR elevation data and multi- and hyperspectral classification results is, in and of itself, a valuable tool for imagery analysis and should be explored further.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2008
Accession Number
AD1107248

Entities

People

  • Michael S. West
  • Ronald G. Resmini

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Altitude
  • Detection
  • Detectors
  • Elevation
  • Hyperspectral Imagery
  • Lidar
  • Materials
  • Mechanical Properties
  • Minerals
  • Phyllosilicates
  • Reflectance
  • Roughness
  • Sea Level
  • Spectra
  • Surface Properties
  • Surface Roughness

Readers

  • Atmospheric Remote Sensing.
  • Geotechnical Engineering.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML