4.7 Article

Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection

Journal

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 108, Issue -, Pages 113-127

Publisher

ELSEVIER
DOI: 10.1016/j.isprsjprs.2015.06.008

Keywords

Stereo imagery; Computer vision; Automation; DEM; Geomorphic change; Landslide

Funding

  1. NSF [1256551, 1304397]
  2. Rocky Mountain NASA Space Grant Consortium (RMNSGC) fellowship
  3. Directorate For Geosciences
  4. Division Of Earth Sciences [1256551, 1304397] Funding Source: National Science Foundation

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Declassified historical imagery from the Hexagon spy satellite database has near-global coverage, yet remains a largely untapped resource for geomorphic change studies. Unavailable satellite ephemeris data make DEM (digital elevation model) extraction difficult in terms of time and accuracy. A new fully-automated pipeline for DEM extraction and image orthorectification is presented which yields accurate results and greatly increases efficiency over traditional photogrammetric methods, making the Hexagon image database much more appealing and accessible. A 1980 Hexagon DEM is extracted and geomorphic change computed for the Thistle Creek Landslide region in the Wasatch Range of North America to demonstrate an application of the new method. Surface elevation changes resulting from the landslide show an average elevation decrease of 14.4 +/- 4.3 m in the source area, an increase of 17.6 +/- 4.7 m in the deposition area, and a decrease of 30.2 +/- 5.1 m resulting from a new roadcut. Two additional applications of the method include volume estimates of material excavated during the Mount St. Helens volcanic eruption and the volume of net ice loss over a 34-year period for glaciers in the Bhutanese Himalayas. These results show the value of Hexagon imagery in detecting and quantifying historical geomorphic change, especially in regions where other data sources are limited. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

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