Abstract

A multi-spectral mid-IR laser imaging study including system engineering, experiments, and image processing and analysis is described. A 4-λ scalable system was built with semiconductor lasers, covering from 3.3-9.6 μm. The X-Y scanning system was capable of 2-dimensional (2D) multi-spectral imaging at a stand-off distance from 13-40 m. The system was applied to diverse targets that consist of man-made and natural materials and objects, and shown capable to resolve and distinguish small spectral differences among the various targets. Colorless objects in the visible were shown with “colorful” signatures in the mid-IR. Image processing algorithm based on spectral contrast was shown most effective to exploit the laser sensitivity and accuracy, as opposed to algorithms that operate mainly on the image spatial intensity. The results also showed the complexity of laser imaging phenomenology, involving both spectroscopic and geometrical scattering effects. A demonstration of 3D multi-spectral imaging was also given. The system design is suitable for compact packages with semiconductor lasers, and the results suggest that laser-based multi-spectral imaging can be a unique and powerful technology for target discrimination.

© 2005 Optical Society of America

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References

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Appl. Opt.

Appl. Phys. Lett.

J.S. Yu, S. Slivken, A. Evans, J. David and M. Razeghi, �??Very High Average Power at Room Temperature from λ ~ 5.9 µm Quantum Cascade Lasers,�?? Appl. Phys. Lett., 82, 3397-3399 (2003).
[CrossRef]

CLEO 2004

Y. Wang, C. Peng., H. Zhang, and H. Q. Le, �??Remote spectral imaging with multi-wavelength and tunable, wavelength-modulation lasers,�?? in Proceedings of the Conference on Laser and Electro-Optics 1, 3 (2004).

Computers & Geosciences

A. Mackiewicz and W. Ratajczak, �??Principal Components Analysis (PCA)�??, Computers & Geosciences 19, 303-342 (1993).

IEEE Signal Processing Magazine

See e. g., IEEE Signal Processing Magazine 19, No 1 Jan (2002).

Opt. Eng.

R. Hardie, M. Vaidyanathan and P. F. McManamon, �??Spectral band selection and classifier design for a multispectral imaging laser radar,�?? Opt. Eng. 37, 752-762 (1998).
[CrossRef]

Z. Morbi, D. B. Ho, H.-W. Ren, H. Q. Le, and S. S. Pei, �??Short-range remote spectral sensor using mid-infrared semiconductor lasers with orthogonal code-division multiplexing approach,�?? Opt. Eng. 41, 2321-2337 (2002).
[CrossRef]

Opt. Express

Proc. SPIE

Y. Wang, Y. W., C. Peng, H. Zhang, A. Seetheraman and H. Q. Le, �??Concepts for Scalable, CDMA-Networked, M/LWIR Semiconductor Laser Standoff Chemical Detection System,�?? in Optically Based Biological and Chemical Sensing for Defence, J. C. Carrano and A. Zukauskas, Eds., Proc. SPIE 5617, 179-189 (2004).
[CrossRef]

T. P. Jannson, P. I. Shnitser, A. A. Kostrzewski, I. P. Agurok, W. Wang, A. Goldsmith, R. M. Kurtz, S. A. Kupiec, G. D. Savant, and J. L. Jannson, �??HWIL LIDAR imaging sensor, 3D synthetic and natural environment, and temporal ATR,�?? in Technologies for Synthetic Environments: Hardware-in-the-Loop Testing VII, R. L. Murrer, ed., Proc. SPIE 4717, 68-76 (2002).
[CrossRef]

A. D. Gleckler, A. Gelbart and J. M. Bowden, �??Multispectral and hyperspectral 3D imaging lidar based upon the multiple slit streak tube imaging lidar,�?? in Laser Radar Technology and Applications VI, G. W. Kamerman, ed., Proc. SPIE 4377, 32-335 (2001).

C. R. Swim, �??Review of active chem-bio sensing,�?? in Chemical and Biological Sensing V, P. J. Gardner, ed., Proc. SPIE 5416, 178-185 (2004).
[CrossRef]

A. Achey, J. Bufton, J. Dawson, W. Huang, S. Lee, N. Mehta, and C. R. Prasad, �??An enhanced multiwavelength ultraviolet biological trigger lidar,�?? in Optically Based Biological and Chemical Sensing for Defence, J. C. Carrano and A. Zukauskas, eds., Proc. SPIE 5617, 87-91 (2004).
[CrossRef]

J. R. Roadcap, P. D. Dao, P. J. McNicholl, �??Case study of multiple-wavelength lidar backscatter from aerosols,�?? in Laser Systems Technology, W. E. Thompson and P. H. Merritt, eds., Proc. SPIE 5087, 156-166 (2003).
[CrossRef]

Other

See e. g. J. B. Campbell, Introduction to Remote Sensing, 2nd ed. (Guilford Press. 1996), Chap. 14.

See e. g. A. Ishimaru �??Wave Propagation and Scattering in Random Media,�?? Academic Press, San Diego, CA (1978).

See e. g., A. A. Kokhanovsky, Optics of Light Scattering Media, 2nd ed. (Praxis Publishing, 2001); T. A. Germer, �??Model Integrated Scattering Tool,�?? <a href="http://physics.nist.gov/Divisions/Div844/facilities/MIST/mist.htm">http://physics.nist.gov/Divisions/Div844/facilities/MIST/mist.htm</a>.

J. R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed. (Prentice Hall, Inc. 1996).

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Figures (11)

Fig. 1.
Fig. 1.

Left: block diagram of the multi-spectral laser imaging system. Right: scattering process.

Fig. 2.
Fig. 2.

(a) Visible image of material targets taped on a wall at 13 m away; (b) MIR raw spectral images at 3.3, 4.9, 7.2, and 9.65 μm plotted individually; (c) IR FPA camera images of the targets 2 m away.

Fig. 3.
Fig. 3.

FTIR spectra of some of the target materials used in Fig. 2. The vertical lines mark the laser wavelengths used in spectral imaging. Some dissimilar materials per chance have very similar spectra at the sampling wavelengths.

Fig. 4.
Fig. 4.

(a) A visible image of the target. (b-d) False color images from the same IR spectral images with different phenomenological algorithms (see text). The algorithms for (b) and (c) lose or distort some spectral information, and some objects become dark. The algorithm for (d) over-classifies and makes more color distinction than physically meaningful. The algorithm for (e) is designed to preserve laser spectral data in lieu of intensity. The resulting false color image has reasonable correlation with the object IR spectra shown in Fig. 3. Notice that colorless objects (black or transparent) in (a) have “colorful” mid-IR signatures in (e).

Fig. 5.
Fig. 5.

Distributions of spectral distance defined in Eq. (2) of a random population of pixels in the spectral images in Fig. 3(b). (a)-(c): increasing the number of wavelengths used in the statistical analysis. The ~7.3-bit entropy is a measure of the “color” amplitude dynamic range and resolution (not color diversity) of these images. It should be taken in the context of how little spectral differences there are among the target objects in Fig. 3. Comparison with log-normal and chi-distribution are also shown.

Fig. 6.
Fig. 6.

(a): Visible image of sand contaminated with oil and water. (b) Colorimetric decomposition shows that the difference between the three marked square spots is not spectral (color) but only intensity (brightness). (c): The MIR multi-spectral false color image makes clear spectral discrimination, and not just intensity discrimination between the spots. The reason is (d): they have distinctive MIR spectra.

Fig. 7.
Fig. 7.

Left: visible image of an aluminum plate contaminated with 4 thin-film stripes of oils. Right: the multi-spectral MIR false-color image showing the oil films as green/blue, the metal as red/yellow. Petrochemical cutting fluid displays a bluish hue that is statistically distinguishable from organic oils.

Fig. 8.
Fig. 8.

(a) Mineral collection; top: visible image; bottom: multi-spectral false-color image (FCI). Sand (quartz) is red, humus soil and woods are brownish/dark green, asphalts are bluish. The beam was ~2.5 cm, and > most pebbles. (b) Sandy soil, humus soil, and leaves. Top: visible; bottom: IR FCI. Sandy and humus soils have different colors. (c) FCI of the same target in (b) under a slightly different arrangement. The green false color of the leaves was coincidental. A barely discernible yellowish spot of a leave became very pronounced in the IR FCI. (d) Household objects. Dried leaves are distinctive from green leaves (black because of weak signals). A piece of wood appears yellow; and concrete appears gray. Other shiny objects with specular reflection cause the dynamic range problem as spectra of weak signals (dark region) are lost.

Fig. 9.
Fig. 9.

(a) Visible image of a variety of food. (b) and (c) False color images (FCI) with slightly different processing algorithms. In (b), a spectral-enhanced IR algorithm discards intensity data, showing some distinction between vegetables and non-vegetables (ham, bread). Shadows on background screen were evidenced. The spectral noise was a result of weak signals. (c) The FCI from an algorithm with intensity included, showing cardboard and concrete distinction, but food appear dark because of low signals.

Fig. 10.
Fig. 10.

(a) Visible image of a scene from 40 m away (~1/2 beam Rayleigh range). (b) False color image (FCI) generated from the 3.3- and 4.9-μm wavelength images in (e) and (f). (c) and (d): Passive infrared focal plane array camera images. The FCI in (b) shows some spectral discrimination (wall, cardboard box, and plastic container) even with only 2 wavelengths.

Fig. 11.
Fig. 11.

(a) Visible image showing an arrangement of three objects, a foam cushion, a cardboard box, and an anodized aluminum optical breadboard. (b) Laser ranging 3D image. (c) False color image (FCI) from MIR multi-spectral images. (d) 3-D image combination of range and FCI. The slope-off at the foam upper edge was due to the large beam spread that struck both the foam and the breadboard.

Tables (1)

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Table 1. MIR semiconductor laser characteristics

Equations (6)

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H S = r P r log 2 ( P r ) ,
S ˜ ( { λ m } m = 1 N ; r i ) S ˜ ( { λ m } m = 1 N ; r j ) σ T [ m w m S ˜ ( λ m ; r i ) S ˜ ( λ m ; r j ) γ ] 1 / γ Noise ( numerator ) for i j
S a ˜ ( { λ m } m = 1 N ; r p ) = S ˜ ( { λ m } m = 1 N ; r p ) S ˜ ( { λ m } m = 1 N ; r p )
S b ˜ ( { λ m } m = 1 N ; r p ) = S a ˜ ( { λ m } m = 1 N ; r p ) + q r p / m ( S a ˜ ( { λ m } m = 1 N ; r p ) + q r p )
S Visible ( R , G , B ; r p ) = C S b ˜ ( { λ m } m = 1 N ; r p ) ;
S False Color ( R , G , B ; r p ) = f A ( r p ) S Visible ( R , G , B ; r p )

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