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White Papers

Reprints and Technical Papers from Applied Analysis Inc.
Click on a title to view an abstract of the research paper.
Some papers are available as links to other sites and some are available for download in Acrobat PDF format.


"Regional Ecosystem Analysis: Puget Sound Metropolitan Area," American Forests, 7/25/98.

"Regional Ecosystem Analysis: Chesapeake Bay Region and Baltimore-Washington Corridor," American Forests, 3/10/99.

"An Evaluation of the Utility of Sub-Pixel Analysis of Thematic Mapper Imagery for the Spruce Beetle Outbreak on the Manti-LaSal National Forest," J. Johnson, P. Greenfield, and A. Steve Munson, published June 23, 1998.

"Automated Scene-Derived Normalization of Spectral Imagery for Subpixel Classification," R. Huguenin, M. Wang, M. Karaska, and K. Roberts, submitted for presentation at SPIE International Symposium on Optical Science, Engineering and Instrumentation, July 1998.

"Utilizing Subpixel Spectral Identification Schemes to Address Emerging Applications Areas," C. Erdman, R. Huguenin, and L Scarff; SPIE Vol. 3119.

"Subpixel Classification of Bald Cypress and Tupelo Gum Trees in Thematic Mapper Imagery," R. Huguenin, M. Karaska, D. Van Blaricom, and J. Jensen; Photogrammetric Engineering & Remote Sensing Vol. 63, pp. 717-725, June 1997.

"Adaptation of the AASAP (IMAGINE Subpixel Classifier) Analysis Software for Automated Bathymetry Mapping," R. Huguenin, E. Boudreau, and M. Karaska; presented at the ERIM Fourth International Conference on Remote Sensing for Marine and Coastal Environments, Orlando, Florida, 17-19 March 1997.

"Nonparametric Classification of Subpixel Materials in Multispectral Imagery," E. Boudreau, R. Huguenin, M. Karaska; SPIE Vol. 2758, 1996.

"Subpixel Analysis Process Improves Accuracy of Multispectral Classifications," R. Huguenin, Earth Observation Magazine, July 1994.

"The Silicate Component of Martian Dust," R. Huguenin, Copyright 1987 by Academic Press, Inc. 0019-1035/87.

"Intelligent Information Extraction from Reflectrance Spectra: Absorption Band Positions," R. Huguenin and J. Jones; Journal of Geophysical Research Vol. 91, No. B9, pp. 9585-9598, August 10, 1986.

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Abstracts

"Regional Ecosystem Analysis: Puget Sound Metropolitan Area," American Forests, 7/25/98.
"
Regional Ecosystem Analysis: Chesapeake Bay Region and Baltimore-Washington Corridor," American Forests, 3/10/99.

Projects Overview
AMERICAN FORESTS conducted a Regional Ecosystem Analysis of the Puget Sound area and a Regional Ecosystem Analysis of Chesapeake Bay Region and Baltimore-Washington corridor to determine how the landscape has changed over time and assess the value of the areas' ecology.

A regional level analysis was conducted of three satellite images spanning a 24-year period from 1972 to 1996. Landsat Multispectral and Thematic Mapper images were used to study several thousand square miles of the watersheds.

The Ecosystem Analyses uses Geographic Information System (GIS) technology to measure the changing structure of the landscape and analyze the scientific and engineering implications of the change. Neighborhood level computer models were developed using CITYgreen software, American Forests’ GIS application for calculating ecosystem benefits. The models represent five typical neighborhood landscapes and measure the effects of these landscapes on storm water and air quality.

The purpose of this project is to document the value of tree-covered landscapes to urban areas. Furthermore, it provides urban decision makers with the information and tools they need to measure the value of natural landscapes and incorporate more trees into future development.


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 "An Evaluation of the Utility of Subpixel Analysis of Thematic Mapper Imagery for the Spruce Beetle Outbreak on the Manti-LaSal National Forest," J. Johnson, P. Greenfield, and A. Steve Munson, published June 23, 1998.

Abstract
Large area mapping and monitoring of forest pest and disease infestations is typically conducted using aerial sketch mapping, and where necessary, ground survey. Both techniques have limited utility in wilderness areas where ground access is difficult and aerial mapping is too costly. The Forest Health Technology Enterprise Team (FHTET) in cooperation with Forest Health Protection (FHP), the Manti-LaSal National Forest and the Remote Sensing Applications Center (RSAC) investigated the utility of subpixel processing for analysis of Landsat Thematic Mapper (TM) imagery of a spruce beetle outbreak.

The study area was on a portion of the Wasatch Plateau area on the Manti-LaSal NF in east-central Utah. Three dates of imagery were acquired and processed using Imagine Subpixel Classification software and the results were compared with existing ground survey data and aerial sketch map data.

The subpixel analysis successfully detected spruce mortality, but did not distinguish between mortality due to spruce beetles versus other mortality, both pest and non-pest in the study area. Subpixel analysis can be an effective supplement to other means of forest health monitoring in species and situations where the geographic extent of the outbreak is too large for standard aerial sketch mapping techniques to adequately document and where the impacted species hold their needles for long periods of time following attack.

Please link me to "An Evaluation of the Utility of Subpixel Analysis of Thematic Mapper Imagery for the Spruce Beetle Outbreak on the Manti-LaSal National Forest."

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"
Automated Scene-Derived Normalization of Spectral Imagery for Subpixel Classification," R. Huguenin, M. Wang, M. Karaska, and K. Roberts, submitted for presentation at SPIE International Symposium on Optical Science, Engineering and Instrumentation, July 1998.

Abstract
Changing illumination and atmospheric conditions hamper the automated analysis of spectral imagery. Applied Analysis Inc. developed an Environmental Correction module as part of its Subpixel Classifier software. This module derives atmospheric and sun angle correction factors directly from an image without the use of predictive models. Subpixel occurrences of dark and bright surface features are used to characterize atmospheric radiance, atmospheric attenuation and sensor transfer functions.

A significant component of each pixel used to derive this information can be unwanted surface reflectance from sun glint, sky illumination, or other solar-illuminated terrain materials. These spectral contributions distort the accurate assessment of atmospheric radiance, atmospheric attenuation and sensor transfer functions. By working at a subpixel level, the Subpixel Classifier software is able to more accurately derive these factors, resulting in improved environmental correction.

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"
Utilizing Subpixel Spectral Identification Schemes to Address Emerging Applications Areas," C. Erdman, R. Huguenin, and L Scarff; SPIE Vol. 3119.

Abstract
The process of extracting information from hyperspectral imagery datasets provided by newer sensor systems can be enhanced through a combination of unique spectral processing algorithms. The first technique we describe is a unique method for extracting the relevant bands within a hyperspectral dataset; this set of optimized bands will provide the greatest potential for discriminating specified materials of interest. The second process, subpixel spectral identification, uses the results from the subset of hyperspectral bands to further refine and distinguish between specific materials of interest, improving classification accuracy and diminishing false alarms. Comparison results produced using the full hyperspectral bandset, a six-band selection chosen based on thematic-mapper band centers, and the optimized bandset are presented for a test scene using HYDICE hyperspectral imagery.

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"Subpixel Classification of Bald Cypress and Tupelo Gum Trees in Thematic Mapper Imagery," R. Huguenin, M. Karaska, D. Van Blaricom, and J. Jensen; Photogrammetric Engineering & Remote Sensing Vol. 63, pp. 717-725, June 1997

Abstract
A subpixel spectral analytical process was used to classify Bald Cypress and Tupelo Gum wetland in Landsat Thematic Mapper imagery in Georgia and South Carolina. The subpixel process enabled the detection of Cypress and Tupelo trees in mixed pixels.

Two hundred pixels were field verified for each tree species to independently measure errors of omission and commission. The cypress total accuracy was 89 percent and the tupelo total accuracy was 91 percent. Field investigations revealed that both cypress and tupelo trees were successfully classified when they occurred both as pure stands and when mixed with other tree species and water.

In a comparison with traditional classification techniques (ISO-DATA clustering, maximum likelihood, and minimum distance) the subpixel classification of cypress and tupelo yielded improved results. Large areas of wetland where cypress was heavily mixed with other tree species were correctly classified by the subpixel process and not classified by the traditional classifiers.

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"Adaptation of the AASAP (n.k.a. Subpixel Classifier) Analysis Software for Automated Bathymetry Mapping," R. Huguenin, E. Boudreau, and M. Karaska; presented at the ERIM Fourth International Conference on Remote Sensing for Marine and Coastal Environments, Orlando, Florida, 17-19 March 1997.

Abstract
The Applied Analysis Spectral Analytical Process (AASAP) has been adapted for automated bathymetric analysis. AASAP is a multispectral image processing software module that performs automated subpixel analysis, i.e.; it is able to detect spectral contributions from materials of interest that may occupy only small fractions of image pixels.

It does this by identifying and removing unwanted spectral contributions from background materials in the pixels. This provides a means for automatically identifying and removing terrain and surface reflection (sky and sun reflection) contributions from water pixels. It also allows composite depths and bottom materials within pixels to be resolved into individual components, e.g., shallow coral and deep sand. This enables more accurate determinations of the water column and bottom reflectance characteristics to be made.

AASAP provides the additional advantage of automatically calculating atmospheric correction factors for the scene being processed. This allows the attenuated bottom radiance to be converted from units of digital number into units of calibrated reflectance, providing a means for automatically calculating depth using a standard regression analysis of logarithmic reflectance.

It also allows signatures derived in one scene to be ported to other scenes. The output of the process includes a pixel fraction and depth for each bottom material per pixel, as well as the mean depth and confidence for each pixel.

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"Nonparametric Classification of Subpixel Materials in Multispectral Imagery," E. Boudreau, R. Huguenin, M. Karaska; SPIE Vol. 2758, 1996

Abstract
An effective process for the automatic classification of subpixel materials in multispectral imagery has been developed. The Applied Analysis Spectral Analytical Process (AASAP) isolates the contribution of specific Materials of Interest (MOI) within mixed pixels.

AASAP consists of a suite of algorithms that perform environmental correction, signature derivation, and subpixel classification. Atmospheric and sun angle correction factors are extracted directly from imagery, allowing signatures produced from a given image to be applied to other images. AASAP signature derivation extracts a component of the pixel spectra that is most common to the training set to produce a signature spectrum and nonparametric feature space. The subpixel classifier applies a background estimation technique to a given pixel under test to produce a residual. A detection occurs when the residual falls within the signature feature space.

AASAP was employed to detect stands of Loblolly Pine in a Landsat TM scene that contained a variety of species of southern yellow pine. An independent field evaluation indicated that 85% of the detections contained over 20% Loblolly, and that 91% of the known Loblolly stands were detected. For another application, a crop signature from a scene in Texas detected occurrences of the same crop in scenes from Kansas and Mexico. AASAP has also been used to locate subpixel occurrences of soil contamination, wetlands species, and lines of communication.

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"
Subpixel Analysis Process Improves Accuracy of Multispectral Classifications," R. Huguenin, Earth Observation Magazine, July 1994.

Synopsis
The reprint provides an overview of how most classification applications can benefit from the Subpixel Classifier’s capabilities. Subpixel analysis is relevant largely because image pixels that contain units or features of interest are, with rare exception, "mixed pixels," i.e.; they contain not only the unit of interest but also other features that contribute to the spectral qualities of the pixel.

Two applications are discussed. The first illustrates the performance of the Subpixel Classifier in "mixed pixel" environment; the other illustrates the ease-of-use of the Subpixel Classifier as a natural extension of conventional multispectral classifiers. The conclusion is that for most applications, the Subpixel Classifier "will enable analysts to improve the accuracy of their current projects by making more complete detections¼ and generate more discriminating classifications."

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"
The Silicate Component of Martian Dust," R. Huguenin, Copyright 1987 by Academic Press, Inc. 0019-1035/87.

Abstract
Absorption features in telescopic reflectance spectra of Mars during 1978 were detected and analyzed. Also detected and analyzed were absorption features in Mariner 7 Infrared Spectrometer and Mariner 9 Infrared Interferometer Spectrometer spectra. The Bands Data Analysis System described by R. L. Huguenin and J. L. Jones (1986, J. Geophys. Res. 91, 9585-9598) was used.

Atmospheric CO2 bands were all detected with an average error of 50 cm-1, providing a test of the sensitivity and accuracy of feature from the Mars data. Absorption features that were attributed to H2O ice were detected in the North Polar Cap region, as well as in regions to the north and east of Hellas basin, and near the Elysium Montes.

Additional absorptions were assigned to structural hydroxyl within a strongly hydrogen-bonded acidic material. Features in the Mariner 9 spectra suggested that the material may be a silicate. Hydroxyl stretch fundamentals were deduced to occur at 2661 and 2824 cm-1, consistent with acidic material having strong hydrogen bonding. In-plane and out-of-plane structural hydroxyl deformation fundamentals were proposed to occur at 1498 and 909 cm-1, respectively, from which a hydrogen bridge length of ~2.4 Å was derived.

Si-Ob (bridging oxygen) and Si-Ot (terminal oxygen) stretch fundamentals were deduced to occur at 1176 and 995 cm-1, respectively. Si-Ob and Si-Ot asymmetric bend fundamentals were deduced to occur near 482 and 397 cm-1, respectively, while a Si-Ob-Si deformation fundamental was proposed to occur near 697 cm-1. These fundamentals suggested that the Si-Ob-Si bond angle may be approximately linear (170o) and that the estimated Si-Ob bridge length may be ~1.61 Å.

The totally symmetric silicate stretch fundamental was deduced to occur near 894 cm-1, from which silicate polymerization equivalent to O/Si = 3.8 ± 0.2 was derived. This is consistent with the derived Si-Ob-Si bond angle and bridge length. An in-plane M-OH libration fundamental near 678 cm-1 was derived, consistent with Ca2+ and/or Mg2+ being the dominant cations in the vicinity of OH-. An analog compound that has a very similar set of structural hydroxyl, silicate, and librational fundamentals is Ca2(HSiO4)OH, with principal differences proposed to be due to the 0.2 ± 0.2 lower O/Si ratio and the 0.08-Å shorter hydrogen bridge length in the Mars material.

Viking compositional data suggest that Mr2+, rather than Ca2+, may be the dominant cation in the Mars material. The differences from the analog phase properties suggest that the material may be similar to H-forsterite (Fo79). It is proposed that the silicate component of the dust may be an incipient alteration (hydrolysis) product of the olivine-rich ultramafic or mafic material, involving a process that resulted in minimum loss of mobile cations and that preserved the high O/Si ratio of the starting material.

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"
Intelligent Information Extraction from Reflectance Spectra: Absorption Band Positions," R. Huguenin and J. Jones; Journal of Geophysical Research Vol. 91, No. B9, pp. 9585-9598, August 10, 1986.

Abstract
A multiple high-order derivative analysis algorithm has been developed that automatically extracts absorption band positions from reflectance spectra. Absorption band positions occur where the fifth derivative of the spectrum equals zero, the fourth derivative has a positive sign, and the second derivative is negative.

The algorithm assumes that bands are approximately symmetric about the band center. Continuum contributions, phase angle effects, and broad low-frequency calibration errors are suppressed. Overlapping bands with centers as close as 0.2-0.5W (full band width at half maximum intensity) can be resolved, as long as bands have comparable widths and intensities.

If overlapping bands are dissimilar, band center separations of 0.6-1.0W are safer limits of resolution. Results are relatively insensitive to whether constituent bands convolve additively or multiplicatively. Spectral resolution can be moderately low, requiring only four to eight data points per W. Errors of derived band centers are <3%W for separations greater than 0.6-1.0W. For overlapping bands with widths of a few thousand cm-1 errors would be typically less than 150 cm-1 from actual band positions.

The band detection algorithm is sensitive to noise, and data smoothing is required. The segment length for smoothing (number of points averaged) needs to be continually adjusted to ~0.5W to minimize signal distortion. A spectral pattern recognition algorithm, which statistically characterizes the frequency distribution of intensity variations in a sliding segment across the spectrum, can be used to predetect the signal spectrum (low-frequency components of the sliding intensity distributions) and to calculate approximate W (predetected W) across the spectrum using the second derivative.

An intelligent control algorithm can then continuously locally adjust the segment lengths for smoothing to 0.5W (predetected W). Smooths are repeated (typically, 20-30 times) until the high-frequency components of the sliding intensity variation distributions across the spectrum are suppressed. A single-pass cubic spline is applied to the smoothed data. The intelligent control algorithm then applies the multiple high-order derivative algorithm.

A sliding segment sixth-order polynomial is fit to the spectrum, with the length of the segment being continuously locally adjust to 1.0W (predetected W) across the spectrum. Adjustment of the segment length to ~1.0W insures that the signal spectrum is minimally distorted and that weak features are not suppressed. Derivatives are calculated for the center point of the sliding segment using the coefficients of the sixth-order polynomial.

The system has successfully extracted band positions from low-quality (6% peak-to-peak noise) synthetic spectra with relatively little degradation of accuracy. Application to natural laboratory and earth-based telescope spectra displayed good reliability and consistency. Processing is fully automated, and the same standardized procedure is followed for all spectra. No continuum removal or band modeling is needed. The automation of analysis could potentially significantly increase the efficiency and yield of information extraction, particularly for high rate repetitive scan laboratory and synoptic remote sensing spectroscopy applications.

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