InfraRed
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The text on this site is published with permission of RAND and taken from "Alternatives of Landmine Detection" Jacqueline MacDonald et.al, RAND report, ISBN 0-8330-3301-8, Document Number: MR-1608-OSTP, Year: 2003[1]
Contents |
Detection with Infrared radiation
Physical Basis:
A large part of the solar energy incident on soil is absorbed, leading to heating. As a result of this heating, the soil emits thermal radiation detectable by a thermal infrared (IR) sensor. Natural solar heating and cooling over a diurnal cycle tend to affect a buried object and the surrounding soil differently, which leads to a detectable temperature difference. For a buried mine this difference arises because the mine is a better thermal insulator than the soil. During the day, the thin layer of soil over the mine tends to accumulate thermal energy because the mine impedes the transport of that heat deeper into the ground. As a result, soil over a mine will tend to be warmer than the surrounding soil. Conversely, in the evening hours, the soil layer over the mine gives up its thermal energy more rapidly than the surrounding soil and it appears cooler. Twice daily the soil over the mine and the background soil will assume the same temperature, making thermal detection impossible. The temperature difference and its temporal behavior depend strongly on a variety of variable natural phenomena, including the time of day, prior solar illumination, wind speed, ground cover, and soil composition (e.g., moisture content). Most thermal detection concepts involve single looks (“snapshots”) of the region of interest. The soil over a mine has different thermal dynamics than homogeneous soil and, as a result, a time sequence of images can often produce better detection than a single image. Hence, staring sensors, which are impractical for many military scenarios, may be attractive for humanitarian demining.
Development Status:
Broadband passive sensors at IR wavelengths are mature and available commercially from several vendors. Algorithms for mine detection, another critical part of any detection system, are somewhat less mature, although a number of groups have reported progress in his area.
Limitations:
Signature variations with time and environmental conditions are a persistent problem for thermal IR mine detection. The optimum time for detection and the expected contrast depend on factors noted above that are often unknown to a remote observer. Surface clutter from reflected light and inhomogeneous soil properties are also problematic. In many cases the size of these clutter artifacts is comparable to that of antipersonnel mines, which leads to false alarms. Thermal emission from foliage (at the temperature of living, respiring vegetation) tends to mask the temperature of the underlying soil (and the thermal mine signature).
Potential for Improvement:
The processes that produce thermal IR target signatures and clutter are poorly understood. In some cases, good detection performance has been demonstrated, but when such systems fail, the reasons for failure are often not evident. A better understanding of target and clutter signatures could substantially improve their effectiveness by allowing them to be deployed appropriately. Staring sensors should also be considered, which take data over an extended period in time, waiting for favorable conditions to arise. Time-history information will also help to compensate for the variability of thermal signatures with time and environmental conditions.
Bibliography / References
- ↑ Jacqueline MacDonald et.al: Alternatives of Landmine Detection, RAND report, ISBN 0-8330-3301-8, Document Number: MR-1608-OSTP, 2003
1. C. Stewart, Summary of Mine Detection Research, Vol. I., Technical Report 1636-TR, Fort Belvoir, Va.: U.S. Army Engineer Research and Development Laboratories (now NVESD), Corps of Engineers, 1960.
2. W. de Jong, H. A. Lensen, and Y. H. L. Janssen, “Sophisticated Test Facility to Detect Landmines,” in Detection and Remediation Technologies for Mines and Minelike Targets IV, A. C. Dubey, J. F.Harvey, J. Broach, and R. E. Dugan, eds., Seattle: International Society for Optical Engineering, 1999, pp.1409–1418.
3. P. Verlinde, M. Acheroy, G. Nesti, and A. Sieber, “First Results of the Joint Multi-sensor Mine-signatures Measurement Campaign (MsMs Project),” in Detection and Remediation Technologies for Mines and Minelike Targets VI, A. C. Dubey, J. F. Harvey, J. T. Broach, and V. George, eds., Seattle: International Society for Optical Engineering, 2001, pp. 1023–1034.
4. B. A. Baertlein and A. H. Gunatilaka, “Optimizing Fusion Architecture for Limited Training Data,” in Detection and Remediation Technologies for Mines and Minelike Targets V, A. C. Dubey, J. F. Harvey, J. Broach, and R. E. Dugan, eds., Seattle: International Society for Optical Engineering, 2000, pp. 804–815. 5. N. Milisavljevic, S. P. van den Broek, I. Bloch, P. B. W. Schwering, H. A. Lensen, and M. Acheroy, “Comparison of Belief Functions and Voting Methods for Fusion of Mine Detection Sensors,” in Detection and Remediation Technologies for Mines and Minelike Targets VI, A. C. Dubey, J. F. Harvey, J. T. Broach, and V. George, eds., Seattle: International Society for Optical Engineering, 2001, pp. 1011–1022.
6. D. H. Chen, I. K. Sendur, W. J. Liao, and B. A. Baertlein, “Using Physical Models to Improve Thermal IR Detection of Buried Mines,” in Detection and Remediation Technologies for Mines and Minelike Targets VI, A. C. Dubey, J. F. Harvey, J. T. Broach, and V. George, eds., Seattle: International Society for Optical Engineering, 2001, pp. 207–218.
