Hyperspectral Imaging

Widespread use of hyperspectral imagery across industries is a relatively recent trend in geospatial analysis. Compared to multispectral sensors (e.g., Landsat, SPOT, AVHRR), which measure reflected radiation from the Earth’s surface at a few widely spaced wavelength bands, hyperspectral sensors measure reflectance over a series of hundreds of narrow and contiguous bands, providing the opportunity for more detailed spectral image analysis. Hyperspectral images are often referred to as image cubes because of their large spectral dimension, in addition to their two spatial dimensions. Figure 3 shows a visualization of an AVIRIS (Airborne Visible/ Infrared Imaging Spectrometer) hyperspectral image taken near Cuprite, Nevada. The visualization is an oblique parallel projection, with the spectral dimension visualized in the xz- and yzplanes (the top and right sides of the cube, respectively). The face of the cube, in the xy-plane, is a false color composite with red, green, and blue bands chosen to emphasize peaks in the reflectance spectra of minerals found in the image, such as buddingtonite, kaolinite, and various clays.


On Tuesday, January 12, 2010, a magnitude 7.0 earthquake struck just miles from Haiti’s capital city of Port-au- Prince. About 3 million people were affected by the quake. The government of Haiti estimated that 250,000 residences and 30,000 commercial buildings were severely damaged or destroyed.

LiDAR can be used to detect and measure objects like collapsed buildings and standing structures damaged by an earthquake. It can also be used in extracting road networks and route planning — information that can be critical for emergency responders trying to plan routes to find people who need help as quickly and efficiently as possible. A 3D visualization, reconstructed from a LiDAR point cloud, showed buildings and roads in Port-au- Prince that were damaged in the January 2010 earthquake.

The data used in producing this visualization were collected in a joint project funded by the World Bank, in conjunction with the Rochester Institute of Technology, the University of Buffalo, and ImageCat, Inc. A twin-engine Piper Navajo, operated by Kucera International, flew missions for seven consecutive days at 3000 feet over Port-au-Prince and other areas badly hit by the earthquake. LiDAR data at 1- and 10-m spatial resolutions were collected to map the disaster zone to aid in crisis management and the eventual reconstruction of the city.

To produce the visualization (see title image), the E3De™ LiDAR processing application was used to extract a digital surface model (DSM) from surface features such as buildings, trees, and cars. Further processing of the DSM gave building footprints and roof shape polygons. Next, a DEM was computed from the DSM using a combination of proprietary crawling and sensitivity algorithms.

Subtracting the DEM from the DSM gives the vertical obstruction layer. With additional image analysis in ENVI™, based on published algorithms in the LiDAR community, intact roads can be separated from structures and debris.

Leveraging 3D LiDAR data, as well as 3D visualization tools for the data, can be invaluable for disaster mitigation. The type of analysis described here can quickly help emergency responders find passable routes to people in need.


In geospatial analysis, 3D visualization techniques are invaluable for enhancing a user’s ability to explore, interpret, and understand data. In the future, as the use of hyperspectral and LiDAR data in disaster management continues to grow, 3D visualization will become increasingly relevant. While the synthesis of hyperspectral and LiDAR data can help emergency responders inventory buildings, land ground teams, find passable routes, and otherwise support crisis response efforts, proper 3D visualization of this data can aid all levels of disaster management, from basic building inventory to sophisticated network routing problems.

This article was written by Mark Piper, Solutions Engineer, Exelis Visual Information Solutions (Boulder, CO). For more information, visit http://info.hotims.com/40437-140.


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  2. Priestnall, G., J. Jaafar and A. Duncan, 2000: Extracting urban features from LiDAR digital surface models. Computers, Environments and Urban Systems, 24, 65-78.
  3. Shippert, P., 2004. Why use hyperspectral imagery?, Photogrammetric Engineering & Remote Sensing, 70(4), 377–380.
  4. Shreiner, D., 2010: OpenGL Programming Guide: The Official Guide to Learning OpenGL, versions .0 and .1. Addison-Wesley, Upper Saddle River, NJ.
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