There is ongoing research in both hardware and software for integrating LiDAR
data with hyperspectral imagery. Many researchers are looking at the possibility of
extending the techniques presented here with other LiDAR sensors. Specifically, a
new sensor development effort, the Coastal Zone Mapping and Imaging LiDAR
(CZMIL), is currently in the flight-testing stage. CZMIL was designed as an
imaging system, and incorporates many hardware changes to improve implementation of data fusion at all levels presented in this chapter. The CZMIL data
processing system, developed in tandem with the airborne hardware, is essentially
a LiDAR/hyperspectral data fusion processing system.
Other areas of research are conversely investigating how hyperspectral information might inform LiDAR processing. One example is using hyperspectral
water column attenuation to aid in processing LiDAR seafloor reflectance imagery
in shallow water, where estimation of water column attenuation is confounded due
to convolution of the surface and bottom returns. Techniques are also under
development for selecting the most information-rich features from the LiDAR data
and hyperspectral imagery, like various texture metrics and spectral indices, for
inclusion in decision-tree, maximum likelihood, and other classifiers.
The types of information provided by integrating hyperspectral imagery with
LiDAR data are not different from those provided by the other remote sensing
techniques described throughout this book. The examples presented here are
specific to LiDAR and hyperspectral, but there is every reason to expect similar
data fusion techniques to be applied to other combinations of remote sensing
products. The advantage and ultimate goal of data fusion is the improved accuracy
of the final coral reef and environmental data products derived from the imagery.
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 OptechInternational), and the University of Southern Mississippi.
Suggested Reading
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 119
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 16
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 62:30–39
7 Integrated LiDAR and Hyperspectral
189
data with hyperspectral imagery. Many researchers are looking at the possibility of
extending the techniques presented here with other LiDAR sensors. Specifically, a
new sensor development effort, the Coastal Zone Mapping and Imaging LiDAR
(CZMIL), is currently in the flight-testing stage. CZMIL was designed as an
imaging system, and incorporates many hardware changes to improve implementation of data fusion at all levels presented in this chapter. The CZMIL data
processing system, developed in tandem with the airborne hardware, is essentially
a LiDAR/hyperspectral data fusion processing system.
Other areas of research are conversely investigating how hyperspectral information might inform LiDAR processing. One example is using hyperspectral
water column attenuation to aid in processing LiDAR seafloor reflectance imagery
in shallow water, where estimation of water column attenuation is confounded due
to convolution of the surface and bottom returns. Techniques are also under
development for selecting the most information-rich features from the LiDAR data
and hyperspectral imagery, like various texture metrics and spectral indices, for
inclusion in decision-tree, maximum likelihood, and other classifiers.
The types of information provided by integrating hyperspectral imagery with
LiDAR data are not different from those provided by the other remote sensing
techniques described throughout this book. The examples presented here are
specific to LiDAR and hyperspectral, but there is every reason to expect similar
data fusion techniques to be applied to other combinations of remote sensing
products. The advantage and ultimate goal of data fusion is the improved accuracy
of the final coral reef and environmental data products derived from the imagery.
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 OptechInternational), and the University of Southern Mississippi.
Suggested Reading
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 119
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 16
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 62:30–39
7 Integrated LiDAR and Hyperspectral
189