Applications of multi-spectral video

Transcription

1 Applications of multi-spectral video Jim Murguia, Greg Diaz, Toby Reeves, Rick Nelson, Jon Mooney, Freeman Shepherd, Greg Griffith and Darlene Franco Solid State Scientific Corporation 27-2 Wright Rd. Hollis, NH ABSTRACT Multi-spectral sensor systems that record spatially and temporally registered image video have a variety of applications depending on the spectral band employed and the number of colors available. The colors can be selected to highlight physically meaningful portions of the image, and the resulting imagery can be used to decode relevant phenomenology. For example, the images can be in spectral bands that identify materials that are intrinsic to the target while uncommon in the backgound, providing an anomaly detection cue. These multi-spectral video sensor engines can also be employed in conjunction with conventional fore-optics such as astronomical telescopes or microscopes to exploit useful phenomenology at dissimilar scales. Here we explore the relevance of multi-spectral video in a space application. This effort coupled a terrestrial multispectral video camera to an astronomical telescope. Data from a variety of objects in Low Earth Orbit (LEO) were collected and analyzed both temporally, using light curves, and spectrally, using principal component analysis (PCA). We find the spectral information is correlated with temporal information, and that the spectral analysis adds the most value when the light curve period is long. The value of spectral-temporal signatures, where the signature is the difference in either the harmonics or phase of the spectral light curves, is investigated with inconclusive results. Keywords: multi-spectral video, light curve, LEO, resident space objects, spectral-temporal light curve. 1. BACKGROUND A collaboration between Solid State Scientific Corporation (SSSC), the Air Force Research Laboratory Space Objects Surveillance Technologies program, and the Magdalena Ridge Observatory (MRO) was formed to acquire and analyze multiband temporal light curves of objects in LEO 1,2. To this end SSSC was tasked with coupling a multi-spectral sensor to the 2.4m telescope at the Magdalena Ridge Observatory, acquiring the data and processing the results. 1.1 The SSSC sensor SSSC has developed a 16-color visible spectral imaging system that is intended for terrestrial spectral imaging applications such as camouflage and dismount detection. The sensor is shown in Fig. 1. It is based on a Kodak KAI CCD 3. It has a 9 o field of view and an optical speed of f/6. The focal plane array can frame at rates up to 110 frames per second. The spectral bands are specified in Table 1, while an example of 16-band image is shown in Fig. 2. Detectors and Imaging Devices: Infrared, Focal Plane, Single Photon, edited by Eustace L. Dereniak, John P. Hartke, Paul D. LeVan, Randolph E. Longshore, Ashok K. Sood, Manijeh Razeghi, Rengarajan Sudharsanan, Proc. of SPIE Vol B 2010 SPIE CCC code: X/10/$18 doi: / Proc. of SPIE Vol B-1

2 Fig. 1. The 16-color visible-near infrared sensor. Table 1. Spectral bands in the multispectral imager. Band No. Wavelength Band No. Wavelength Band No. Wavelength Band No. Wavelength nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm Fig. 2. An example of imagery taken with the multispectral sensor. 1.2 The 2.4m telescope at the Magdalena Ridge Observatory The MRO fast-tracking 2.4-meter telescope is a new high-technology facility located at 10,612 ft. in the Magdalena Mountains in central New Mexico (see Fig. 3)4. The MRO observatory is fourth highest observatory in the world, and it has one of the largest telescopes in the world with a primary mission of characterizing small bodies in the Solar System. The 2.4-meter telescope is capable of fast and accurate non-sidereal tracking with slew rates up to 15o/sec making it an ideal instrument for studying fast moving objects in Low Earth Orbit (LEO). The 2.4-meter telescope can accommodate a wide variety of instrument systems, and supports the fabrication, integration, and operation of new instrumentation as well as the development of new and innovative observational techniques. Proc. of SPIE Vol B-2

3 Fig. 3. The Magdalena Ridge Observatory. The telescope is a modified Ritchey-Chrétien design, with an overall optical speed of f/8.8 and Field-of-View (FOV) of 18 arcmin. There are two Nasmyth ports (which can support larger and heavier instrumentation) and four bent Cassegrain ports, one of which permanently hosts a Shack-Hartmann wavefront sensor to facilitate automatic collimation and focusing. Therefore, the facility can simultaneously mount up to 5 instruments at any given time. Median good seeing at the site is 0.7 arc-seconds, and the faintness limit of the telescope for visual wavelengths is about 25th magnitude. 1.3 The coupling optic The coupling optic mates the MRO telescope to the multi-spectral sensor. It collimates the rays from the telescope and images the pupil of the telescope onto that of the multi-spectral sensor. The optical assembly for coupling the MRO telescope to the SSSC visible multi-spectral sensor is a 43mm f/8.4 optic designed to work over the um spectral band with a 4.4 degree field of view. An illustration of the optical layout is shown in Fig. 4. The optical performance of the coupler is illustrated in Fig. 5. The performance of the entire telescope-coupler-sensor system is illustrated in Fig. 6, while the completed package on the telescope is shown in Fig. 7. Fig. 4. Layout of the optical coupler. Proc. of SPIE Vol B-3

4 Fig. 5. Performance of the coupler. Spot diagrams are shown on the left, while the MTF is shown on the right. Fig. 6. The performance of the complete system including the telescope, coupler and multi-spectral sensor. Fig. 7. The coupler and multi-spectral sensor mounted at one of the Nasmyth ports of the MRO 2.4m telescope. Proc. of SPIE Vol B-4

5 2. RESULTS The experiment schedule extended over 8 days, from September 20th to 27th 2007, during which time approximately 104 data sets were obtained. These sets included star calibrations, dark current, and other calibration measurements. Approximately 67 of the 104 data collects were obtained for hard target bodies. Many of these were not analyzed due to clouds or other anomalies in the measurements. Temporal, spectral and spectral-temporal analysis was performed and is reported below. 2.1 Temporal analysis: Light Curves The signature observed from earth of unresolved objects in LEO depends on the object itself and on the object's orientation relative to both the sun and the observer. The observed signal changes as the orientation of the object progresses through its orbital motion. Rotating or tumbling objects can have periodic signatures that are characterized by their light curve, which is a graph of the observed signal as a function of time. Light curves can be used to distinguish between objects and to assess the physical configuration of an object. Examples of four light curves from the collect are shown in Fig. 8. In Fig. 8 it is evident that the period alone is useful discriminant. Fig. 8. Light curves from four dissimilar objects. Each colored line represents a different spectral band. The top-left object is a calibration star with no observed periodicity. Top-right as a periodicity of approximately 12s. That of the bottom left and right are 1.6s and 1.03s. Proc. of SPIE Vol B-5

6 High fidelity representations of the Light Curves that improve the signal relative to the noise can be obtained by summing multiple periods ( ) = 1 ( + ) where 0 <. Examples of a high fidelity single period are shown in Fig. 9 and Fig. 10. In Fig. 9 two single-period light curves with a similar period are compared. This makes it easy to see the salient features of the single period that is repeated throughout the bottom two frames of Fig. 8. In this case the presence of three distinct peaks, one smaller than the other two is evident on the left, while two very sharp dips are evident in the right two frames. In the lower frames of Fig. 9 each of the curves is scaled to a common mean. The similarity between the curves indicates little spectral-temporal information is present. In Fig. 10 four different instances of a signature with the same base period are shown. Each has the same double peak; however, the magnitude of the smaller peak varies among the sets. Fig. 9. One period for the two objects represented at the bottom of Fig. 8. Here many periods have been summed to reduce the noise. Each line represents a different spectral band. The curves in the top row are normalized to facilitate comparison in the bottom row. Proc. of SPIE Vol B-6

7 Fig. 10. Four variations of light curves with the same period indicating a variation in the object orientation. The magnitude of the small peak relative to the two larger peaks is variable. 2.2 Spectral analysis The spectral analysis is implemented as a Principal Component Analysis (PCA). PCA is usually used to address linear mixing, which is common in spectral imaging. Here linear mixing is not expected; however we do expect variations in illumination and substantial temporal noise. We should see similar datasets clustering in the output of the PCA. The PCA proceeds by ignoring the temporal variation. Each spectral band is averaged over time and the results are formed into a row vector for each data set. = (, ), (, ), (, ), Proc. of SPIE Vol B-7

8 where (, ) is the signature of event k at time t row vectors are then combined into a single matrix A, where = Once the data has been reduced to a matrix, the matrix is decomposed using Singular Value Decomposition (SVD) = where V is orthonormal, w is diagonal, and U is column orthonormal. Typically, a few of the elements in w dominate the other terms and the columns of U that correspond to the dominant values in w are used to create a scatter plot, as shown in Fig. 11. The plot shown in Fig. 11 demonstrates clustering of similar events with some degree of overlap between clusters. Assessment of spectral discrimination based on clustering of light curves with similar periods (as shown in Fig. 11) may be misleading, since objects with similar light curves are not necessarily similar. While in many cases objects with the same period to be similar, such is not always the case. For example, there is no reason objects with infinite period (e.g. stars) should all have the same spectral characteristics; indeed, the color temperature of stars exhibits a wide variation and a corresponding variation is observed (see the green symbols in Fig. 11, which correspond to the constant light curves). Further, variations in the illumination could introduce spectral variations that would not be reflected in the period of the light curve. For example, the red symbols in Fig. 11 correspond to objects with light curves similar to the top row in Fig. 10, while the yellow symbols correspond to objects with light curves similar to the bottom row in Fig. 10. While the period of these five objects is the same, these spectral differences are reflected in the light curves and probably represent physically meaningful systematic variations in viewing rather than noise. Spectral information is useful for objects with little temporal variation, appears to correlate with variations in the light curves and may provide information that is not available from a single polychromatic light curve. Fig. 11. A scatter plot of the two major Principal Components (PC0 vs. PC1). Each marker corresponds to a dataset. Markers with the same color have similar Light Curves. Green corresponds to light curves similar to the upper left of Fig. 8, black corresponds to the upper right, red is the lower left while blue is the lower right. Proc. of SPIE Vol B-8

9 2.3 Spectral-temporal analysis The goal of spectral-temporal analysis in this application is to uncover wavelength dependent harmonics in the light curves. While the period of the light curves should be independent of wavelength, a colored panel or other spectral feature on a rotating body will introduce either harmonics or a phase shift in the fundamental. An indication of the potential of spectral-temporal analysis is shown in Fig. 12 where a phase shift is observed. Fig. 12. Illustration of a candidate spectral-temporal signature. The yellow and magenta curves appear to lead the green and blue curves. While this effect is consistently observed, the result is not conclusive due to the low signal to noise level. Proc. of SPIE Vol B-9

10 This type of feature is consistent with a colored panel or region on a rotating body. It could be used to assess the orientation of the rotational axis or to determine the state of deployable panel or attachment. Since the spectral-temporal signature is a second-order term, averaging of multiple light curves may be required to detect signals with low chromatic contrast. 2.4 Resolved Targets The International Space Station (ISS) was a convenient resolved target that was used to demonstrate the imaging capabilities of the sensor. Both a grayscale and a color image are shown in Fig. 13. This pseudo-color image was obtained by mapping three bands to red, green and blue and shows that there is spatial structure to the chromatic characteristics. If the ISS were spinning, one would expect a substantial spectral-temporal signature. Fig. 13. The International Space Station observed with the SSSC/MRO multi-spectral sensor. CONCLUSIONS SSSC has successfully coupled an existing multi-spectral sensor to the MRO 2.4m telescope. The resulting sensor was used to collect spectral-temporal data from a variety of objects in LEO. Temporal analysis of unresolved targets proved to be consistent with the widely known observation that both the period and waveform of the light curve are powerful discriminants. Spectral analysis of the LEO objects was correlated with the temporal result in the sense that objects with similar light curves clustered in the spectral scatter plot. This indicates that spectral processing may be useful in discrimination even for high-signal light curves; however, spectral content is believed to be most beneficial when there is no discernable period, or when the amplitude of the temporal modulation is small. The utility of spectral-temporal processing was explored without compelling results; however, the resolved image of the ISS indicates that spectral-temporal data may be useful under conditions where the chromatic variations in the spatial structure are modulated by motion of the spacecraft. Future work should address the performance of the sensor, which was not designed for low-background observation. A spectral sensor designed specifically for space observation would have a lower noise floor and may have a larger field of view. Proc. of SPIE Vol B-10

11 ACKNOWLEDGEMENTS We gratefully acknowledge the support of Air Force Research Laboratory, Space Vehicles Directorate, Space Objects Surveillance Technologies program team. The team includes James Brown, Phan Dao, Patrick J. McNicholl, Justin E. Cowley, Mike J. Kendra and Peter Crabtree. We would like to thank Eileen Ryan and William Ryan for support on site. Melanie Weeks and Paul Hofmann of the AFRL Sensors Directorate encouraged us to work with the Space Vehicles Directorate. REFERENCES 1. Dao, P. D., P. J. McNicholl, J. H. Brown, J. E. Crowley, M. J. Kendra, P. N. Crabtree, A. V. Dentamaro, E. V. Ryan, and W. Ryan, Space Object Characterization with 16-Visible-Band Measurements at Magdalena Ridge Observatory, Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, Alcala, C. M., and Brown, J. H., Space Object Characterization Using Time-Frequency Analysis of Multispectral Measurements from the Magdalena Ridge Observatory, Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, ( 0340ProductSummary.pdf) 4. ( Proc. of SPIE Vol B-11