Abstract

Target acquisition infrared imaging sensors are characterized by their minimum resolvable temperature parameter that is translated to the probability of identification (Pid) performance estimate for a given target. Intelligence–surveillance–reconnaissance (ISR) sensors are characterized by the general image quality equation to give a national imagery interpretability rating scale (NIIRS) performance estimate. Sensors, such as those on Predator and Global Hawk, will soon be used for both ISR and target acquisition purposes. We present a performance conversion that includes both sensor resolution and sensitivity. We also provide the first empirical results to our knowledge ever to be presented that relate NIIRS and Pid for a given set of targets.

© 1999 Optical Society of America

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References

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  1. R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
    [CrossRef]
  2. J. Johnson, “Analysis of image-forming systems,” in Proceedings of the Image Intensifier Symposium (Warfare Vision Branch, Electrical Engineering Department, U.S. Army Engineering Development Laboratories, Ft. Belvoir, Va., 1958), pp. 249–273.
  3. J. Lloyd, Thermal Imaging Systems (Plenum, New York, 1975), p. 183.
  4. R. Sendall, F. Rosell, “EO sensor performance analysis and synthesis (TV/IR comparison study), (U.S. Air Force Avionics Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, 1973).
  5. J. Ratches, “NVL static performance model for thermal viewing systems,” (U.S. Army Electronics Command, Fort Monmouth, N.J., 1975).
  6. J. C. Leachtenauer, “National imagery interpretability rating scales: overview and product description,” in APRS/ASCM Annual Convention and Exhibition Technical Papers: Remote Sensing and Photogrammetry (American Society for Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping, Baltimore, Md., 1996), Vol. 1, pp. 262–272.
  7. J. Leachtenauer, W. Malila, J. Irvine, L. Colburn, N. Salvaggio, “General image quality equations: GIQE,” Appl. Opt. 36, 8322–8328 (1997).
    [CrossRef]

1998 (1)

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

1997 (1)

Colburn, L.

Cox, P.

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

Driggers, R.

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

Irvine, J.

Johnson, J.

J. Johnson, “Analysis of image-forming systems,” in Proceedings of the Image Intensifier Symposium (Warfare Vision Branch, Electrical Engineering Department, U.S. Army Engineering Development Laboratories, Ft. Belvoir, Va., 1958), pp. 249–273.

Leachtenauer, J.

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

J. Leachtenauer, W. Malila, J. Irvine, L. Colburn, N. Salvaggio, “General image quality equations: GIQE,” Appl. Opt. 36, 8322–8328 (1997).
[CrossRef]

Leachtenauer, J. C.

J. C. Leachtenauer, “National imagery interpretability rating scales: overview and product description,” in APRS/ASCM Annual Convention and Exhibition Technical Papers: Remote Sensing and Photogrammetry (American Society for Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping, Baltimore, Md., 1996), Vol. 1, pp. 262–272.

Lloyd, J.

J. Lloyd, Thermal Imaging Systems (Plenum, New York, 1975), p. 183.

Malila, W.

Ratches, J.

J. Ratches, “NVL static performance model for thermal viewing systems,” (U.S. Army Electronics Command, Fort Monmouth, N.J., 1975).

Rosell, F.

R. Sendall, F. Rosell, “EO sensor performance analysis and synthesis (TV/IR comparison study), (U.S. Air Force Avionics Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, 1973).

Salvaggio, N.

Scribner, D.

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

Sendall, R.

R. Sendall, F. Rosell, “EO sensor performance analysis and synthesis (TV/IR comparison study), (U.S. Air Force Avionics Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, 1973).

Vollmerhausen, R.

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

Appl. Opt. (1)

Opt. Eng. (1)

R. Driggers, P. Cox, J. Leachtenauer, R. Vollmerhausen, D. Scribner, “Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation,” Opt. Eng. 37, 789–797 (1998).
[CrossRef]

Other (5)

J. Johnson, “Analysis of image-forming systems,” in Proceedings of the Image Intensifier Symposium (Warfare Vision Branch, Electrical Engineering Department, U.S. Army Engineering Development Laboratories, Ft. Belvoir, Va., 1958), pp. 249–273.

J. Lloyd, Thermal Imaging Systems (Plenum, New York, 1975), p. 183.

R. Sendall, F. Rosell, “EO sensor performance analysis and synthesis (TV/IR comparison study), (U.S. Air Force Avionics Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, 1973).

J. Ratches, “NVL static performance model for thermal viewing systems,” (U.S. Army Electronics Command, Fort Monmouth, N.J., 1975).

J. C. Leachtenauer, “National imagery interpretability rating scales: overview and product description,” in APRS/ASCM Annual Convention and Exhibition Technical Papers: Remote Sensing and Photogrammetry (American Society for Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping, Baltimore, Md., 1996), Vol. 1, pp. 262–272.

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Figures (4)

Fig. 1
Fig. 1

Tactical acquisition process. TTPF, target transfer probability function.

Fig. 2
Fig. 2

GIQE results: NIIRS as a function of range.

Fig. 3
Fig. 3

Targets used.

Fig. 4
Fig. 4

Tactical-to-ISR relationship converting Pid to NIIRS and comparison with an empirical study and FLIR92 ACQUIRE and GIQE models.

Tables (1)

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Table 1 Examples of NIIRS Criteria for Ratings Levels

Equations (14)

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PN=NN502.7+0.7NN501+NN502.7+0.7NN50.
MRT=1/HfSNF F#2πfBWBL1/2δF0Dλpeak*2teηeff1/2τSL,
ηeff=NscanNdAdetFOVH×FOVV×Fo2,
BW=W2-HNfHefHWf2df square centimeters, BL=L2-HNfHefHLf2df square centimeters, SL=L2- [HfHefHL2df square centimeters, δ=ΔλLλ, T/TSλdλ watts/cm2 sr Kelvin,
ρ2-D=fxfy.
N=ρ dcR,
NIIRS=10.251-a log10 GSDGM+b log10 RERGM-0.656H-0.344G/SNR,
ERxx=0.5+1π0MTFxfxfxsin2πfxxdfx
RERGM=ERx0.5-ERx-0.5ERy0.5-ERy-0.51/2.
GSDx=pixel pitchfocal lengthslant rangecos θ.
GSDGM=GSDxGSDy sin α1/2.
G=i=1Mj=1N kij21/2,
GSDmin=1010.251+b log10 RERGM - NIIRS - 0.656H-0.344G/SNRa.
N=dc2GSD.

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