ALGORITHMS FOR TARGET DISCRIMINATION AND CONTRAST ENHANCEMENT USING NARROWBAND POLARIMETRIC IMAGE DATA M. J. Duggin1,2 and R. Loe3 1
Professor, Department of Environmental Resources and Forest Engineering, SUNY, Syracuse, NY 13210. E-mail:
[email protected] or
[email protected]. 2
3
PAR Government Systems Corporation, 314 South Jay Street, Rome, NY 13440.
PAR Government Systems Corporation, 314 South Jay Street, Rome, NY 13440. Email:
[email protected]. ABSTRACT
There is evidence that polarimetric contrast differences in images are band-dependent. Some previous evidence is reviewed. We fabricated and tested a hyperspectral imaging polarimeter that we reported previously1. This device uses a tunable liquid crystal filter and a 16-bit camera. The polarimeter is designed to work in the visible and in the near infrared spectral region. We present here some examples of imagery collected with this sensor, and show how this data may be used to provide superior target discrimination if used selectively, with the appropriate algorithms. Keywords: polarimetry, polarization, hyperspectral, Stokes, sensors, algorithms.
INTRODUCTION Duggin and Loe1 described a hyperspectral imaging polarimeter. This further extends multiband polarimetric image acquisition and analysis discussed previously2,3. With such an instrument, polarization image data can yield information additional to that accessible from radiometric intensity, wavelengthdependence of intensity and contrast, texture, topography, or feature shape, size or relative disposition. The potential exists for improvements in feature extraction. For example, hyperspectral imaging in the intensity domain gives information on vegetation stress4. To further explore the potential of hyperspectral polarimetry for feature mapping, especially in natural scenes involving vegetation5, we carefully calibrated a hyperspectral imaging polarimeter based upon an astronomical camera and a tunable liquid crystal filter. We discuss some of the calibration and feature extraction procedures that we performed. Polarized radiation may be described by means of four parameters, known as Stokes parameters. G.G. Stokes, in 1852, introduced four quantities that are functions only of the measurable parameters of electromagnetic waves and are known as Stokes parameters. Generally, radiometric (intensity) information is contained within the first Stokes parameter, while information on the degree of linear polarization may be determined from the second and third Stokes parameters even if we use a thin film linear polarizer to make the measurements. Relatively few researchers have explored the possibility of using polarization as a source of contrast information for feature extraction. Little has so far been done to study the wavelengthdependence of polarization in the visible and near infrared regions. Walraven6 used a 35mm camera to obtain sets of four slides of scenes using a polarizing filter at 0o, 45o, 90o and 135o relative orientation of the polarization direction about the optic axis of the camera. He measured the photographic density of the emulsion and related this to scene radiance. The intensity of the radiation that passes through the polarization filter is given by
Polarization Analysis, Measurement, and Remote Sensing IV, Dennis Goldstein, David Chenault, Walter Egan, Michael Duggin, Editors, Proceedings of SPIE Vol. 4481 (2002) © 2002 SPIE · 0277-786X/02/$15.00
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I′ (θ) = 1/2 (I + Q cos 2θ + U sin 2θ )
(1)
where θ is the angle of the transmission axis of the polarizer about the optic axis of the camera, with respect to the horizontal. If measurements are successively made with θ =0o, 45o, 90o and 135o, then the resulting intensity is I′ (0) = 1/2 (I + Q) (2) I′ (45) = 1/2 (I + U)
(3)
I′ (90) = 1/2 (I - Q)
(4)
I′ (135) = 1/2 (I -U)
(5)
From these equations, Walraven6 deduced the Stokes parameters in terms of the sum and difference images: I = S0 = 1/2 [I′ (0) + I′ (45) + I′ (90) + I′ (135)]
(6)
Q = S1 = I′ (0) - I′ (90)
(7)
U = S2 = I′ (45) - I′ (135)
(8)
With the polarizer set at each of the four angles defined above, the four photographic images were obtained with the same exposure. They were then digitized and registered. Images of I, and of the degree of linear polarization (DOLP or P), were obtained, where P is given to close approximation by the expression, assuming that circular polarization is small:
1 P = (Q 2 + U 2 ) 1/ 2 I
(9)
Walraven showed that the polarization images provided new and useful information about a scene beyond that available from an intensity image. It was only later, using digital camera data with a linear response and 8-bit or better radiometric precision, that it was possible to measure scene radiance at low levels with sufficient accuracy to relate polarization to scene radiance5,7. Estimates of the upper limit of circular polarization (S3) cannot be accurately obtained using thin film polarizers. Polarization can yield image information additional to that accessible from radiometric intensity, wavelength-dependence, texture, topography, or feature shape, size or relative disposition. Duggin2 discussed the separation of camouflage netting from vegetation background by use of the chromaticity of polarimetric images obtained with a three-band camera. Egan8 lists the spectrophotometric properties of some farm crops and soils, but this was for small samples. Polarization characteristics will differ for small samples and for a full canopy. Egan’s results showed that for low albedo laboratory samples, the polarization can be as high as 100%. However, polarization of a vegetation canopy is greatly affected by morphology and by shadowing, which in turn are impacted by slope, aspect and sun elevation and azimuth. Egan also showed that the degree of polarization is wavelength-dependent. Vanderbilt, et al 9-11 measured the degree of polarization of reflected light from corn, soybean and sorghum leaves. They found that as the angle of incidence approached 60 degrees, the degree of polarization approached 60%. They suggested that the primary source of polarization was light reflected specularly from the leaf surface. Grant12 gives a useful summary of polarized light measurements on leaves. She points out that the primary cause of specularity in a leaf is the cuticle. Grant further states that the outermost cuticular layer is epicuticular wax, which may be amorphous, semicrystalline or crystalline12. Baum, et al13 point out that the distribution, form, and microdetails of epicuticular waxes on mature leaves are genus-specific. Kharuk and Yegorov14 report that in the waveband 1.75 to 2.2 micrometers, water stress and dust deposition on leaves caused measurable change in the polarization of radiance reflected from the affected canopy. Wickland15 points out that a significant amount of light does not enter the leaf but is reflected specularly. The specular 248
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component of the light reflected from the leaf surface is polarized. The degree of polarization depends on the sun-leaf-sensor geometry, the leaf disposition and canopy morphology, wavelength, the index of refraction of the epicuticular wax, and the surface roughness characteristics of the epicuticular wax. If the specular component of light reflected from the canopy could be separated from the total reflected component, then the remaining diffuse component would contain information on leaf chemistry. Vanderbilt, et al9-11, point out that polarization depends on canopy morphology, and upon the leaf surface characteristics. Since canopy morphology, and leaf chemistry will be impacted by stress, vigor and growth stage, it is reasonable to expect that the degree of polarization of reflected radiation from a plant canopy will be expected to contain information on the degree of stress, vegetation type, vigor and growth stage. The morphology and spectropolarimetric properties of vegetation differ from those of camouflage netting and of man-made objects, as does their geometric anisotropy (spectropolarimetric indicatrix). Duggin1 presents plots of the polarimetric chromaticity (three-band polarimetric image data) vs. image intensity for regions of interest consisting of pixels located on various scene components vs. radiometric intensity in each band. We anticipate that new information might be obtained from hyperspectral polarimetric studies performed across the red edge and the blue edge. This will aid in vegetation discrimination, vegetation stress discrimination and vegetation vs. non-vegetation discrimination. It will also aid in the discrimination of man-made objects from vegetation.
EXPERIMENTAL DATA AND ANALYSIS In order to measure the subtle spectral polarimetric detail, which might be useful in feature detection and quantification, we need improved spectral sensitivity. We decided to use a tunable liquid crystal filter, which was adjustable with a 10 nm bandpass across the spectral range 400 –700 nm. This filter was attached to the front of a 50 mm focal length lens, which was attached to a 16-bit 512 by 512 pixel focal plane array, designed for astronomical photography. We calibrated for radiometric fall-off with increasing field angle, a possible wavelength-dependence of this effect, inherent polarization of the tunable filtercamera combination, which might be wavelength-dependent, and possible anisotropies in these effects within the image plane1. It was considered that these effects might, in turn be wavelength-dependent. We previously described1 a calibration cylinder which provided uniform (to better than 2%) irradiance on standard gray card and Spectralon targets. The arrangement is shown diagrammatically in figure 1.
4” Diameter
Opening
15” Diameter
23” Height
18% Grey Card Diffuse Light Source
Outside
Interior
Figure1. Calibration cylinder. The interior was diffuse white reflector and the camera viewed the calibration target normally, through the aperture.
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In subsequent measurements, Spectralon reflectors, and other standards were substituted for the gray calibration panel. In experiments to check the inherent polarization of the camera/filter system, a protractor scale was fitted to the top of the cylinder, concentric with the aperture, so that a sheet of HN38S Polaroid linear polarizer could be rotated about the optic axis of the camera/filter and positioned with better than 10. Checks with a 10 spotmeter across the field of view showed less than 2% variation across the field of view for a uniform reflector. In one laboratory experiment, which we describe here, we fabricated a matte black cylinder topped with a matte black circular platform on which live leaves were placed. A black matte base was at the bottom of the cylinder. A model of a Bradley Fighting Vehicle was placed at the base of the cylinder. The polarimeter viewed the model through a circular aperture as shown in Figure 2. Illumination was diffuse. Measurements were made across the red edge with and without a polarizer. It should be borne in mind that in field observations, the global spectral irradiance will be only partly diffuse, depending on atmospheric conditions, cloud cover and view and illumination geometry, so that spectropolarimetric contrasts could very well be greater than we show here.
Figure 2. The model Bradley viewed through an aperture in the leaves. The illumination is diffuse. Measurements of the intensity at a range of wavelengths spanning the red edge are shown in figure 3. It is seen that there is some wavelength-dependence of the intensity contrast, but little detail can be discerned at any wavelength, although the images obtained at 600 nm and at 660 nm do seem to provide the best contrast. If we look at the S1 images obtained at the same wavelengths as shown in figure 4, then we can immediately see that there is more contrast between the vehicle and its surroundings than is obtained using the unpolarized intensity images. If we examine the (1-DOLP) image (DOLP being the acronym for degree of linear polarization) shown in figure 5, then we can see that the contrast between the model vehicle and its shadowed and vegetated background is greatly enhanced. Furthermore, we can see that the contrast is wavelength-dependent. The quantity (1-DOLP) is sometimes referred to as depolarization, and may be seen to yield far more information on the contrast between a feature of interest and background than the unpolarized (no polarizer) intensity image shown in figure 3. In order to make this contrast clearer, we show a comparison between the unpolarized and the (1-DOLP) images at wavelengths spanning the red edge in figures 6-9.
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The differential with respect to wavelength images and vegetation index images were also investigated. The normalized difference vegetation image is the difference of the recorded radiance in a NIR bandpass minus the radiance recorded in a visible bandpass, divided by the sum of these quantities. NDVI images were calculated for the intensity images obtained with no polarizer, and for the corresponding depolarization (1DOLP) images. A comparison for images obtained at 600 nm and at 800 nm is shown in figure 10. Figure 11 shows the detail of the NDVI formed from the (1-DOLP) images at 600 nm and at 800 nm. It is clear that much more information is evident in the image derived from polarimetric data.
Figure 3. Intensity images obtained in laboratory at wavelengths spanning the red edge. No polarizer was used in obtaining these images. The irradiance is diffuse.
Figure 4. S1 images obtained in the laboratory at wavelengths spanning the red edge. Here a linear polarizer was used to obtain the Stokes image set. The irradiance is diffuse.
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Figure 5. (1-DOLP) images obtained in the laboratory at wavelengths spanning the red edge. . Here a linear polarizer was used to obtain the Stokes image set. The irradiance is diffuse.
Figure 6. A comparison of the unpolarized and (1-DOLP) images obtained at 600 nm with diffuse irradiation.
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Figure 7. A comparison of the unpolarized and (1-DOLP) images obtained at 690 nm with diffuse irradiation
Figure 8. A comparison of the unpolarized and (1-DOLP) images obtained at 720 nm with diffuse irradiation
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Figure 9. A comparison of the unpolarized and (1-DOLP) images obtained at 800 nm with diffuse irradiation
Figure 10. A comparison of the NDVI images formed from the 600 nm and 800 nm bandpasses using unpolarized intensity and depolarization (1-DOLP) image data.
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Figure 11. The NDVI formed from the (1-DOLP) images obtained at 600 nm and 800 nm.
CONCLUSIONS AND RECOMMENDATIONS We have described previous work, which has led to the construction of an imaging polarimeter1, based upon a 16-bit camera and a tunable filter. We present examples of image data collected in the laboratory with diffuse irradiance, which show that spectral polarimetric data is superior to unpolarized intensity data in facilitating the extraction of detail in shadowed regions below a vegetative canopy. However, the choice of processing method and algorithm selection is critical in obtaining optimum feature detection and recognition.
REFERENCES 1. M. J. Duggin and R. Loe, “The acquisition of multiband polarimetric imagery: calibration considerations”, SPIE vol 4133, 238-248, 2000. 2. M. J. Duggin, “Imaging polarimetry in scene element discrimination”, SPIE vol. 3754, 108-117, 1999. 3. L. J. Denes, M. Gottlieb, B. Kaminsky and D. F. Huber, ”Spectro-polarimetric imaging for object recognition”, SPIE 3240, 8-18, 1998.
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4. W. Philpot, M. J. Duggin, R. Raba, and Fu-an Tsai, “Analysis of reflectance and fluorescence spectra for atypical features: fluorescence in the yellow-green”, Jol of Plant Pathology, vol 148, 567-573, 1996. 5. M. J. Duggin, G. J. Kinn and M. Schrader, “Enhancement of vegetation mapping using Stokes parameter images”, Proc. SPIE Conference on Polarization: Measurement, Analysis and Remote Sensing, 30 July-1 August, San Diego, California. 1997, pp 307-313. 6. Walraven, R., “Polarization imagery,” Optical Engng., vol. 20, pp. 15-18. 1981 7. Duggin, M. J., G. J. Kinn and E. Bohling, “Vegetative target enhancement in natural scenes using multiband polarization methods”, ”, Proc. SPIE Conference on Polarization: Measurement, Analysis and Remote Sensing, 30 July-1 August, San Diego, California. 1997. Pp288-295. 8. Egan, W. G., Photometry and Polarization in Remote Sensing, Elsevier, New York, 1985. 9. Vanderbilt, V.C., L.Grant, and C.S.T. Daughtry, “Polarization of light scattered by vegetation,” Proceedings of IEEE, vol. 73, pp. 1012-1024, 1985. 10. Vanderbilt, V. C., L. Grant, L. L. Biehl and B. F. Robinson, “Specular, diffuse and polarized light scattered by two wheat canopies,” Applied. Opt., 24: 2408-2418, 1985. 11. Vanderbilt, V. C. and L. Grant, “Polarization photometer to measure bidirectional reflectance factor,” Opt. Engng., 25: 566-571, 1986. 12. Grant, L., “Diffuse and specular properties of leaf reflectance,” Remote Sens. Environ., 22: 309-322., 1987. 13. Baum, B. R., A. P. Tullock and L. G. Bailey, “A survey of epicuticular waxes among some species of Triticum. 1. Ultrastructure of glumes and some leaves as observed with the scanning electron microscope,” Can. J. Bot., 58: 2467-2480, 1980. 14. Kharuk, V. I., and Yegorov, V. V., “Polarimetric indication of plant stress,” Remote Sens. Environ., 33: 35-40, 1990. 15. Wickland, D. E., “Future directions for remote sensing in terrestrial ecological research,” In: Theory and Applications of Optical Remote Sensing (pp. 691-724), Ed. G. Asrar. Wiley Interscience, New York. 1989.
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