Spectroscopic MASINT

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Spectroscopic MASINT measurement and signature intelligence can be applied either to targets that are already excited, such as an engine exhaust, or stimulated with a laser or other energy source. It is not an imaging technique, although it can be used to extract greater information from images. It is an application of the electro-optical MASINT discipline.

Where an imagery intelligence IMINT sensor would take a picture that fills a frame, a spectroscopic MASINT sensor gives a list, by coordinate, of wavelengths and energy. Multispectral IMINT is likely to discriminate more wavelengths, especially if it extends into the infrared (IR) or ultraviolet (UV) spectral range, than a human being, even one with an excellent color sense, could discriminate.

This discipline complements IMINT, by, for example, detecting false-color camouflage. Simulated foliage painted green may appear normal to human color vision, but spectrometry can detect the lack of chlorophyll.

Categorizing spectra

The results plot energy versus frequency. A spectral plot represents radiant intensity versus wavelength at an instant in time. The number of spectral bands in a sensor system determines the amount of detail that can be obtained about the source of the object being viewed. Sensor systems range from

Type Number of bands Status
Multispectral 2-100 Operational on U-2 aircraft
hyperspectral 100 to 1,000 bands Advanced development
ultraspectral more than 1,000 bands Research

More bands provide more discrete information, or greater resolution. The characteristic emission and absorption spectra serve to fingerprint or define the makeup of the feature that was observed. A radiometric plot represents the radiant intensity versus time; there can be plots at multiple bands or wavelengths. For each point along a time-intensity radiometric plot, a spectral plot can be generated based on the number of spectral bands in the collector, such as the radiant intensity plot of a missile exhaust plume as the missile is in flight. The intensity or brightness of the object is a function of several conditions including its temperature, surface properties or material, and how fast it is moving. .[1] Remember that additional, non-electro-optical sensors, such as ionizing radiation detectors, can correlate with these bands.

Advancing optical spectroscopy was identified as a high priority by a National Science Foundation workshop[2] in supporting counterterrorism and general intelligence community needs. These needs were seen as most critical in the WMD context. The highest priority was increasing the sensitivity of spectroscopic scanners, since, if an attack has not actually taken place, the threat needs to be analyzed remotely. In the real world of attempting early warning, expecting to get a signature of something, which is clearly a weapon, is unrealistic. Consider that the worst chemical poisoning in history was an industrial accident, the Bhopal disaster. The participants suggested that the "intelligence community must exploit signatures of feedstock materials, precursors, by-products of testing or production, and other inadvertent or unavoidable signatures." False positives are inevitable, and other techniques need to screen them out.

Second to detectability, as a priority was rejecting noise and background. It is especially difficult for biowarfare agents, which are the greatest WMD challenge to detect by remote sensing rather than laboratory analysis of a sample. Methods may need to depend on signal enhancement, by clandestine dispersion of reagents in the area of interest, which variously could emit or absorb particular spectra. Fluorescent reactions are well known in the laboratory; could they be done remotely and secretly? Other approaches could pump the sample with an appropriately tuned laser, perhaps at several wavelengths. The participants stressed that the need to miniaturize sensors, which might enter the area in question using unmanned sensors, including miniaturized aerial, surface, and even subsurface vehicles.

Electro-optical spectroscopy is one means of chemical detection, especially using nondispersive infrared spectroscopy is one MASINT technology that lends itself to early warning of deliberate or actual releases. In general, however, chemical sensors tend to use a combination of gas chromatography and mass spectrometry, which are more associated with materials MASINT. See Chemical Warfare and Improvised Chemical Devices.

Laser excitation with multispectral return analysis is a promising chemical and possibly biological analysis method.[3]

Multispectral MASINT

SYERS 2, on the high-altitude U-2 reconnaissance aircraft, is the only operational airborne military multi-spectral sensor, providing 7 bands of visual and infrared imagery at high resolution.[3] It was built by Itek, now a subsidiary of Hughes. [4]

This system can transmit its data in real time, as well as recording it. Future upgrades are expected to increase the downlink to 274 Mbps and processed imagery to 650 Mbps.

Hyperspectral MASINT

Hyperspectral MASINT involves the synthesis of images as seen by visible and near infrared light. US MASINT in this area is coordinated by the Hyperspectral MASINT Support to Military Operations (HYMSMO) project. This MASINT technology differs from IMINT in that it attempts to understand the physical characteristics of what is seen, not just what it looks like.[5]

Hyperspectral imaging typically needs multiple imaging modalities, such as whiskbroom, pushbroom, tomographic, intelligent filters, and time series.

Implementation Issues

Some of the major issues in visible and infrared hyperspectral processing include atmospheric correction, for the visible and short wave infrared.[6] (0.4 - 2.5 micrometer) dictate sensor radiances need to be converted to surface reflectances. This dictates a need for measuring, and connecting for:

  • atmospheric absorption and scattering
  • aerosol optical depth,
  • water vapor,
  • correction for the effect of bi-directional reflectance distribution function,
  • blurring due to the adjacency effect and retrieval of reflectance in shadows.

Hyperspectral, as opposed to multispectral, processing gives the potential of improved spectral signature measurement from airborne and spaceborne sensor platforms. Sensors on these platforms, however, must compensate for atmospheric effects. Such compensation is easiest with high contrast targets sensed through well-behaved atmosphere with even, reliable illumination, the real world will not always be so cooperative. For more complicated situations, one can not simply compensate for the atmospheric and illumination conditions by taking them out. The Invariant Algorithm for target detection was designed to find many possible combinations of these conditions for the image.[7]


Multiple organizations, with several reference sensors, are collecting libraries of hyperspectral signatures, starting with undisturbed areas such as deserts, forests, cities, etc.

  • AHI, the Airborne Hyperspectral Imager,[8] a hyperspectral sensor operating in the long-wave infrared spectrum for DARPA’s Hyperspectral Mine Detection (HMD) program. AHI is a helicopter-borne LWIR hyperspectral imager with real time on-board radiometric calibration and mine detection.
  • COMPASS, the Compact Airborne Spectral Sensor, a day-only sensor for 384 bands between from 400 to 2350 nm, being developed by the Army Night Vision and Electronic Sensors Directorate (NVESD)[3].
  • HyLite, Army day/night Hyperspectral Longwave Imager for the Tactical Environment Cite error: Closing </ref> missing for <ref> tag built by Hughes Danbury Optical Systems and flight tested on a Convair 580.
  • SPIRITT, the Air Force's Spectral Infrared Remote Imaging Transition Testbed,[9] a day/night, long range reconnaissance imaging testbed composed of a hyperspectral sensor system with integrated high resolution imaging

Signature Libraries

Under the HYMSMO program, there have been a number of studies to build hyperspectral imaging signatures in various kinds of terrain.[10] Signatures of undisturbed forest, desert, island and urban areas are being recorded with sensors including COMPASS, HYDICE and SPIRITT. Many of these areas are also being analyzed with complementary sensors including synthetic aperture radar (SAR).

Hyperspectral Signature Library Development
Operation/Environment Date Location
Desert Radiance I[11] October 1994 White Sands Missile Range, New Mexico
Desert Radiance II June 1995 Yuma Proving Grounds, Arizona
Forest Radiance I[12] (also had urban and waterfront components) August 1995 Aberdeen Proving Grounds, Maryland
Island Radiance I[13] (also had lake, ocean and shallow water components) October 1995 Lake Tahoe, California/Nevada; Kaneohe Bay, Hawaii

A representative test range, with and without buried metal, is the Steel Crater Test Area at the Yuma Proving Grounds.[14] This was developed for radar measurements, but is comparable to other signature development areas for other sensors and may be used for hyperspectral sensing of buried objects.


In applications of intelligence interest, the Johns Hopkins University Applied Physics Laboratory (JHU/APL) has demonstrated that hyperspectral sensing allows discrimination of refined signatures, based on a large number of narrow frequency bands across a wide spectrum.[15] These techniques can identify include military vehicle paints, characteristic of particular countries' signatures. They can differentiate camouflage from real vegetation. By detecting disturbances in earth, they can detect a wide variety of both excavation and buried materials. Roads and surfaces that have been lightly or heavily trafficked will produce different measurements than the reference signatures.

It can detect specific types of foliage supporting drug-crop identification; disturbed soil supporting the identification of mass graves, minefields, caches, underground facilities or cut foliage; and variances in soil, foliage, and hydrologic features often supporting NBC contaminant detection. This was done previously with false-color infrared photographic film, but electronics are faster and more flexible. [5]

Minefield detection

JHU/APL target-detection algorithms have been applied to the Army Wide Area Airborne Minefield Detection (WAAMD) program’s desert and forest. By using the COMPASS and AHI hyperspectral sensors, robust detection of both surface and buried minefields is achieved with very low false alarm rates.

Underground Construction

Hyperspectral imaging can detect disturbed earth and foliage. In in concert with other methods such as coherent change detection radar, which can precisely measure changes in the height of the ground surface. Together, these can detect underground construction.

While still at a research level, Gravitimetric MASINT can, with these other MASINT sensors, give precise location information for deeply buried command centers, WMD facilities, and other critical target. It remains a truism that once a target can be located, it can be killed. "Bunker-buster" nuclear weapons are not needed when multiple precision guided bombs can successively deepen a hole until the no-longer-protected structure is reached.

Urban Spectral Target Detection

Using data collected over US cities by the Army COMPASS and Air Force SPIRITT sensors, JHU/APL target detection algorithms are being applied to urban hyperspectral signatures. The ability to robustly detect unique spectral targets in urban areas denied for ground inspection, with limited ancillary information will assist in the development and deployment of future operational hyperspectral systems overseas.[15]

Mass Graves

Peace operations and war crimes investigation may require the detection of often-clandestine mass graves. Clandestinity makes it difficult to get witness testimony, or use technologies that require direct access to the suspected grave site (e.g., ground penetrating radar). Hyperspectral imaging from aircraft or satellites can provide remotely sensed reflectance spectra to help detect such graves. Imaging of an experimental mass grave and a real-world mass grave show that hyperspectral remote imaging is a powerful method for finding mass graves in real time, or, in some cases, retrospectively.[16]

Ground Order of Battle Target Detection

JHU/APL target detection algorithms have been applied to the HYMSMO desert and forest libraries, and can reveal camouflage, concealment and deception protecting ground military equipment. Other algorithms have been demonstrated, using HYDICE data, that they can identify lines of communication based on the disturbance of roads and other ground surfaces.[15]

Biomass Estimation

Knowing the fractions of vegetation and soil is of helps estimate the biomass. Biomass is not extremely important for military operations, but gives information for national-level economic and environmental intelligence. Detailed hyperspectral imagery such as the leaf chemical content (nitrogen, proteins, lignin and water) can be relevant to counterdrug surveillance.[17]


  1. US Army (May 2004), Chapter 9: Measurement and Signals Intelligence, Department of the Army
  2. (August 2003). Approaches to Combat Terrorism (ACT): Report of a Joint Workshop Exploring the Role of the Mathematical and Physical Sciences in Support of Basic Research Needs of the U.S. Intelligence Community. National Science Foundation.
  3. 3.0 3.1 3.2 Office of the United States Secretary of Defense. Unmanned Aircraft Systems Roadmap 2005-2030.
  4. SENIOR YEAR Electro-Optical Reconnaissance System [SYERS], Federation of American Scientists
  5. 5.0 5.1 Gatz, Nahum (February 23, 2006). Hyperspectral Technology OverviewNASIC Distinguished Lecture Series in Remote Sensing, Wright-Patterson Air Force Base, Dayton, Ohio: Center for MASINT Studies and Research.
  6. Goetz, Alexander (February 3, 2006). Hyperspectral Remote Sensing of the Earth: Science, Sensors and ApplicationsNASIC Distinguished Lecture Series in Remote Sensing, Wright-Patterson Air Force Base, Dayton, Ohio: Center for MASINT Studies and Research.
  7. Gold, Rachel (May 2005). Performance Analysis of the Invariant Algorithm for Target Detection in Hyperspectral Imagery.
  8. Lucey, P.G. et al.. An Airborne Hyperspectral Imager for Hyperspectral Mine Detection.
  9. Spectral Infrared Remote Imaging Transition Testbed (December 21, 2000).
  10. Bergman, Steven M. (December 1996). The Utility of Hyperspectral Data in Detecting and Discriminating Actual and Decoy Target Vehicles. US Naval Postgraduate School.
  11. Fay, Matthew E. (1997), An Analysis of Hyperspectral Data collected during Operation Desert Radiance, US Naval Postgraduate School
  12. Olsen, R.C. (1997). Target detection in a forest environment using spectral imagery. US Naval Postgraduate School.
  13. Stuffle, L. Douglas (December, 1996), Bathymetry by Hyperspectral Imagery, US Naval Postgraduate School
  14. Clyde C. DeLuca; Vincent Marinelli , Marc Ressler, and Tuan Ton. Unexploded Ordnance Detection Experiments Using Ultra-Wideband Synthetic Aperture Radar.
  15. 15.0 15.1 15.2 Kolodner, Marc A.. Hyperspectral Exploitation Program at the Johns Hopkins University Applied Physics Laboratory. Cite error: Invalid <ref> tag; name "Kolodner" defined multiple times with different content
  16. Kalacska, M. (March 2006). "Remote Sensing as a Tool for the Detection of Clandestine Mass Graves". Canadian Society of Forensic Science Journal 39 (1).
  17. Borel, Christoph C. (July 17, 2007), Challenging Image Analysis Problems in the Exploitation of Hyperspectral Remote Sensing Data for the Visible and Infrared Spectral Region, Wright-Patterson Air Force Base, Dayton, Ohio: Center for MASINT Studies and Research