Michael E. Winter

Physics-based resolution enhancement of hyperspectral data

Hyperspectral imaging systems are assuming a greater importance for a wide variety of commercial and military systems. The reason for this increased interest is the fact that a hyperspectral sensor of a give4n spatial resolution or pixel sized will reveal information on the scene that are not obtainable by single band or multi-spectral sensors. There have been several approaches to using a single higher spatial resolution band to improve the spatial resolution fo the hyperspectral data. In this paper, a new technique for improving the spatial resolution of hyperspectral image data will be presented. This technique, called Joint End-member Determination and Unmixing, combines a high-resolution image with a lower spatial resolution hyperspectral image to produce a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the panchromatic image. Instead of using statistical methods to sharpen hyperspectral imagery, a physical model is used where the data present in both the hyperspectral and high-resolution data are assumed to follow linear mixing model. In this paper, the new mixture model based resolution enhancement approach will be compared to the statistical approach using data from NASA/JPL AVIRIS hyperspectral sensor.

Three years of operation of AHI: the University of Hawaii's Airborne Hyperspectral Imager

The AHI sensor consists of a long-wave infrared pushbroom hyperspectral imager and a boresighted 3-color visible high resolution CCD linescan camera. The system used a background suppression system to achieve good noise characteristics (less than 1(mu) fl NESR). Work with AHI has shown the utility of the long-wave infrared a variety of applications. The AHI system has been used successfully in the detection of buried land mines using infrared absorption features of disturbed soil. Recently, the AHI has been used to examine the feasibility active and passive hyperspectral imaging under outdoor and laboratory conditions at three ranges. In addition, the AHI was flown over a coral reef ecosystem on the Hawaiian island of Molokai to study fresh water intrusion into coral reef ecosystems. Theoretical calculations have been done propose extensions to the AHI design in order to produce an instrument with a higher signal to noise ratio.

Hyperspectral image sharpening using multispectral data

Multispectral sharpening of hyperspectral imagery fuses the spectral content of a hyperspectral image with the spatial and spectral content of the multispectral image. The approach we have been investigating compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-resolution hyperspectral image to produce a sharpened product. We evaluate this technique against several types of data, showing good performance across with all data sets. Recent improvements in the algorithm allow target detection to be performed without loss of performance even at extreme sharpening ratios.

Performance of the AHI airborne thermal infrared hyperspectral imager

The AHI sensor consists of a long-wave infrared pushbroom hyperspectral imager and a boresighted 3- color visible high resolution CCD linescan camera. The system used a background suppression system to achieve good noise characteristics (less than 1µfl NESR). Work with AHI has shown the utility of the longwave infrared a variety of applications. The AHI system has been used successfully in the detection of buried land mines using infrared absorption features of disturbed soil. Gas detection was also shown feasible, with gas absorption being clearly visible in the thermal IR. This allowed the mapping of a gas release using a matched filter. Geological mapping using AHI can be performed using the thermal band absorption features of different minerals. A large-scale geological map was obtained over a dry lake area in California using a mosaic of AHI flightlines, including mineral spectra and relative abundance maps.

Automated material map generation using hyperspectral data: a case study using AVIRIS imagery

The University of Hawaiis Efficient Materials Mapping program aims to automatically and rapidly produce material maps from hyperspectral scenes. The program combines an end- member determination algorithm and a material identification algorithm to produce context maps in real time without user intervention. The material identification algorithm is a combination of a spectral databse and analytic code; each spectrum in the library augmented with computer readable diagnostic instructions. At present, the material library consists of over three hundred different spectra, generally geological materials from the USGS digital spectral library, however selected spectra from other libraries have been incorporated. Our method has been applied to an AVIRIS sceme taken over Kaneohe Bay, Hawaii. This scene contains large expanses of ocean, developed and undeveloped land, thus providing a good test bed for the program. The results of applying this methodolgy were verified by ground truth where possible by team equipped with hand held spectrometer. Algorithm derived archetypical en-member locations were well matched well by the material identification database, however the end-member determination itself operated sub- optimally on this scene. These results will guid progress with respect to the continued development of this program.

Efficient materials mapping for hyperspectral data

Hyperspectral data rates and volumes challenge analysis approaches that are not highly automated and efficient. Derived products from hyperspectral data, which are presented in units that are physically meaningful, have added value to analysts who are not spectral or statistical experts. The Efficient Materials Mapping project involves developing an approach that is both efficient in terms of processing time and analyzed data volume and produces outputs in terms of surface chemical or material composition. Our approach will exploit the typical redundancy inherent in hyperspectral data of natural scenes to reduce data volume. This data volume reduction is combined with an automated approach to extract chemical information from spectral data. The results will be a method to produce maps of chemical quantities that can be readily interpreted by analysts specializing in characteristics of terrains and targets rather than photons and spectra.

Image Factorization for Small Object Detection

Blind source separation techniques, specifically independent components analysis and Nonnegative Image Factorization have seen increasing use in the hyperspectral community for automated image exploitation. These techniques differ from more traditional image reduction methods such as principal components in that they make different statistical assumptions as to the nature of the image. As such, these techniques provide the potential for the development of exploitation techniques that better preserve spectral information associated with small targets that tends to be lost with more traditional statistical processing.

Analysis of data from a thermal infrared hyperspectral instrument

While reflection band hyperspectral instruments have been in use for over a decade, only recently has data from airborne thermal IR hyperspectral instruments become available. One such instrument is the Airborne Hyperspectral Imager (AHI). AHI is a pushbroom sensor developed by the University of Hawaii that spans the 8 to 11.5 micrometer spectral band with 32 spectral bands and 256 simultaneous spatial channels. While many analysis techniques used for reflection band hyperspectral processing can be applied to the thermal band, new procedures had to be developed. In particular, sensor noise and sensor non-linearity induced spectral artifacts are a greater problem than for the VNIR and SWIR. The process begins with calibration, with different calibration files being used to optimize the reduction of sensor artifacts such as shading and striping. Once the data has been calibrated to radiance units, the absorption and path radiance effects of the atmosphere can be removed, if atmospheric truth is available. Following this step, the apparent emissivity is calculated for every pixel in each band. The data is now in a form that is analogous to the apparent reflectance images developed for reflection band data. At this point spectral analysis techniques can be applied to classify the image. The procedure used here was to use an automated endmember determination algorithm, N- FINDR, to determine spectral endmembers and unmix the data cube into fractional abundances. Since some endmembers are likely to result from residual sensor and cultural artifacts, the automated endmember determination and unmixing procedure is performed interactively to optimize results. Both the fractional abundance planes and the endmember spectra themselves are then reviewed for artifacts. Selected abundance planes that correspond to real minerals can then be combined into a classification map. In this paper, AHI data collected for two applications: the detection of buried land mine application and a geological remote sensing application will be presented using similar processing steps.

Preliminary results from AHI at the MUST 2000 experiment in Cairns, Australia

The MUlti Sensor Trial 2000 experiment was a multi-platform remote sensing deployment in Cairns Australia. Included in the deployment were both visible and infrared airborne hyperspectral images. The University of Hawaiis Airborne Hyperspectral Imager represented the thermal infrared portion of the data collect. The ability to discriminate various targets using the thermal infrared was explored. Consequent data processing involved separating targets from clutter using matched filters. In addition, a preliminary atmospheric correction algorithm was developed based on the ISIS algorithm used in SEBASS.