AAI's Software Tool Descriptions

• Quantitative Shoreline Characterizer 2 (QSC2): In-house software advances the state-of-the-art in remote sensing by using multispectral or hyperspectral imagery to derive accurate pixel-resolution measurements of water quality and depth over wide geographic areas without the need for ground truth data. Specifically, QSC2 uses four spectral bands (Red, Green, Blue, and Near Infrared) to measure:
  • Suspended Chlorophyll (Chl)
  • Suspended Minerals (SM)
  • Colored Dissolved Organic Carbon (CDOC)
  • Depth (when bottom is visible from surface)
• QSC2 also derives four additional water quality parameters from the above measurements:
  • Turbidity (measure of water clarity)
  • Secchi Depth (measure of water clarity)
  • Subsurface Object Sighting Distances (horizontal and vertical)
  • Trophic Status Indicator (derived from chlorophyll concentration)

  QSC2 derives each of the above parameters for each pixel in an image as a depth-integrated value (i.e., averaged across a water column extending from the surface to the bottom, or to as deep as light can penetrate the water and reflect back to the surface).

  QSC2 offers dramatic advantages in cost, speed, and coverage area over surface-based, field-data collection methods. For example, it can automatically analyze very large geographic areas in a single satellite image (up to 10,000 square miles in a Landsat image).

• QSC2 is ideally suited for:
  • Monitoring and mapping water quality and depth
  • Detecting and mapping anomalies/pollutants that are spectrally apparent in large water bodies
  • Locating sources of water pollution
  • Supporting environmental impact studies and litigations
  • Supporting EPA-required monitoring for compliance with the Clean Water Act

  QSC2 can also plot long-term trends in the parameters it derives using existing Landsat imagery available worldwide from 1982 to the present at 16-day intervals, when local cloud cover is not significant. For short-term trends, QSC2 can be used with other satellite imagery, (IKONOS, SPOT 5, Orbview 3, QuickBird, IRS, etc.) which are available as often as every one to three days.

• QSC2 is unique in that:
  • It is self-calibrating (using AAI's Image Calibrator as described below) and thus, requires no ground truth data.
  • It automatically compensates for four sources of measurement errors:
    • Atmospheric propagation effects on spectral imagery
    • Sun and sky reflections from the water surface
    • Land-water, mixed-pixel effects
    • Variations in benthic material (sea, lake, or river bottoms)
  • It detects Chlorophyll where traditional remote sensing software cannot:
    • In shallow water; not just in deep water
    • In the presence of suspended minerals and organic carbon

  QSC2 runs on a Sun Solaris workstation under UNIX.

  Download QSC2 Brochure in .PDF
  Download QSC2 Client Base for water quality analysis projects
  

• Generalized Benthic Material Characterizer (GBMC): In-house software characterizes and maps benthic materials (sea, lake, or river bottoms) in water bodies where the bottom is visible from the surface. GBMC is advanced prototype software that uses reflective spectra measured by multispectral and/or hyperspectral sensors in satellites or aircraft to characterize bottom material as one or more general types, such as:
  • Unconsolidated sediment
  • Hard-bottom (rock or reef)
  • Submerged aquatic vegetation (SAV)
  The definitions of general bottom types can be customized for each application.

  GBMC has shown promise based on initial testing for the National Geospatial-Intelligence Agency (NGA), and is currently implemented as an add-on module to AAI's QSC2 tool.

  For more detailed characterization and mapping of specific types of bottom material (e.g., specific types of sediment, hard bottom, or vegetation), AAI uses its Subpixel Classifier software and its Supervised Anomaly Finder (SAF) software, as described below.

• Subpixel Classifier (SC): Advances the state-of-the-art in remote sensing by using multispectral or hyperspectral imagery to detect and quantify the presence of a specific material of interest (MOI) at subpixel resolution. Specifically, SC:
  • Detecting specific soil and crop types and assessing crop health
  • Detecting specific vegetation species and assessing health
  • Geologic resource exploration for specific mineral deposits (oil, gas, etc.)
  • Detecting and mapping small waterways, unimproved roadways, disturbed soil, illicit crops, etc.
  • Detecting military targets and camouflaged objects
  SC is unique in that it:
  • Is more accurate than whole-pixel or other subpixel classifiers
  • SC increases the effective spatial resolution of the best sensors
  • When cost is critical, SC can often achieve required results using lower-resolution, lower-cost imagery
  • Derives a "pure" sample spectral signature of an MOI (i.e., each sample signature is 100% MOI) to train its classification algorithm
    • Traditional classifiers use "mixed" sample signatures of the MOI (i.e., sample signatures contain some non-MOI material)
  • Automatically corrects imagery for atmosphere-induced errors
    • This makes SC's spectral signatures reusable from scene-to-scene for different dates, times and location

SC runs on a Sun Solaris workstation under UNIX or on a PC under Windows NT, 2000, or XP-Pro. SC is available as a fully-integrated plug-in module to Leica Geosystems' ERDAS IMAGINE geospatial imaging software suite. SC is also available to the spectral intelligence community in a government-controlled software package named COSMEC.

Download SC Brochure in .PDF.
Download SC Client Base for land cover analysis projects
Download Whole-pixel Client Base for land cover analysis projects.


• Supervised Anomaly Finder (SAF): In-house software extends the utility of AAI's Subpixel Classifier software for analyzing satellite imagery at the subpixel level. SAF enables:
  • User-tunable detection/identification of image anomalies
  • User-tunable detection of image changes
  • Small object detection with lower false alarm rates
    
    

  SAF differs from Subpixel Classifier in that SAF requires no sample spectral signature of a material of interest. Instead, SAF discovers new materials of interest (anomalies) and groups them into categories with similar characteristics.

  SAF can be used for a much more detailed characterization and mapping of benthic materials than AAI's Generalized Benthic Material Characterizer (GBMC) software (described above). In particular, SAF can be customized to identify and map specific types of:
  • Unconsolidated sediment
  • Hard-bottom
  • Submerged Aquatic Vegetation (SAV)

  Also, SAF has the potential to expand the capability of AAI's QSC2 tool from simply identifying suspended minerals in general, to discriminating between different types of suspended minerals, based on their spectral signatures. AAI could implement this capability as a straight-forward modification and refinement of SAF's current capabilities.

• Image Calibrator (IC): In-house software used by AAI's QSC2 tool to automatically calibrate satellite-based multispectral and hyperspectral sensor imagery without the need for ground-truth data by autonomously modeling the radiated and reflected energy observed by the sensor using pixel data alone, and then calibrating the image by converting the digital number units of raw pixel spectra to apparent reflectance. IC also corrects for haze effects in the image.

• BANDS Analyzer: In-house software analyzes the absorption and emission features in the spectra of a material and uses them to automatically identify the material. BANDS is the core technology used in AAI's Hyperspectral Distiller tool, described below, and also is a core technology used in COSMEC software for the Department of Defense.

• Hyperspectral Distiller (HD): In-house software converts hyperspectral data to nominally equivalent multispectral data for more cost-effective processing without sacrificing discrimination sensitivity. HD uses AAI's BANDS Analyzer to scan all spectral bands recorded by a hyperspecteral sensor and automatically selects the most relevant bands for detecting objects/scenes, or detecting changes in objects/scenes.

• Geodata Visualization System (GVS): A suite of Java applets that allow a large number of users in remote locations to interact with large online databases of spatial imagery via the web to extract information without the need for specialized software or training.

AAI is also a reseller of Leica Geosystems' complete line of ERDAS IMAGINE geographic imaging software, for image orthorectification, atmospheric correction, surface interpolation, mosaicking, mapping, 3D visualization, spatial data modeling, and advanced image analysis, such as the AAI-developed IMAGINE Subpixel Classifier Tool described above.