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Integrated LiDAR and Hyperspectral

  • Jennifer M. Wozencraft
  • Joong Yong Park
Chapter

Abstract

Integrating LiDAR data and hyperspectral imagery is an area of active research in remote sensing, inclusive of application for coastal and coral reef mapping. These two technologies can be combined in a number of different ways, and at a number of stages of processing to produce benthic classification maps. This chapter introduces the concept of data fusion, presents a data fusion model, and describes the different ways in which LiDAR and hyperspectral data can be integrated for benthic mapping. Examples are presented to first demonstrate data fusion during the preprocessing stage prior to classification, followed by data fusion performed during processing and classification. The chapter concludes with examples of how classification maps derived from LiDAR data and hyperspectral imagery individually can be combined in a postprocessing high-level fusion approach to produce an integrated benthic classification map.

Keywords

Data Fusion LiDAR Data Radiative Transfer Equation Hyperspectral Data Hyperspectral Imagery 
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.

Notes

Acknowledgments

The data collection, data processing, and data fusion technique development summarized in this chapter were funded by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) through the Naval Oceanographic Office’s Adding Hyperspectral to CHARTS Project, the U.S. Army Corps of Engineers National Coastal Mapping Program, and the National Ocean Partnership Program’s High-level Data Fusion Software for SHOALS-1,000TH project; and by the U.S. Naval Research Laboratory’s Countermine Lidar UAV-Based System Project. The data collection, data processing, and data fusion technique development summarized in this chapter were accomplished by personnel at JALBTCX, Optech, Inc. (USA, formerly Optech International), and the University of Southern Mississippi.

Suggested Reading

  1. Lee M (2003) Benthic mapping of coastal waters using data fusion of Hyperspectral Imagery and Airborne Laser Bathymetry. Ph.D. dissertation. University of Florida. Gainsville, Florida, p 119Google Scholar
  2. Park JY, Ramnath V, Feygels V, Kim M, Mathur A, Aitken J, Tuell GH (2010) Active-passive data fusion algorithms for seafloor imaging and classification from CZMIL data. In: Lewis PE (eds) Proceedings SPIE, 7,695. Shen SS, Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery 16Google Scholar
  3. Reif M, Macon CL, Wozencraft JM (2011) Post-katrina land-cover, elevation, and volume change assessment along the south shore of lake pontchartrain, Louisiana. J Coast Res Appl Lidar Tech [Pe’eri, Long] USA 62:30–39Google Scholar
  4. Tuell GH, Park JY, Aitken J, Ramnath V, Feygels VI, Guenther GC, Kopilevich YI (2005) SHOALS-enabled 3-D benthic mapping. In: Chen S, Lewis P, (eds) Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery 11, Proceedings of SPIE 5806:816–826Google Scholar
  5. Wozencraft JM, Macon CL, Lillycrop WJ (2008) High resolution coastal data for Hawaii. Proceedings of sessions of the conference: solutions to coastal disasters, Am Soc Civ Eng, pp 422–431Google Scholar

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Joint Airborne lidar Bathymetry Technical Center of Expertise, Coastal and Hydraulics LaboratoryU.S. Army Corps of Engineers, Engineer Research and Development CenterKilnUSA
  2. 2.Optech, IncKilnUSA

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