A novel dual-band hyperspectral imager has been developed to collect 128-band hyperspectral image cubes simultaneously in both 4-5.25 μm (mid wave IR, MWIR) and 8-10.5 μm (long wave IR, LWIR) bands for both target detection and standoff detection of chemical and biological agents. The imager uses a specially designed diffractive optics Ge lens with a dual-band 320×240 HgCdTe infrared (IR) focal plane array (FPA) cooled with a closed cycle Sterling-cooler. The diffractive optics lens acts both as a focusing as well as a dispersive device. The imager simultaneously collects a single-color full scene image with a narrow band in the LWIR region (e.g., at 8 μm) using the first order diffraction and corresponding single-color image in the MWIR region (e.g., at 4 μm) using the second order diffraction. Images at different wavelengths are obtained by moving the lens along its optical axis to focus the corresponding wavelengths. Contributions of out of focus wavelengths are removed in post processing. In this paper we will briefly discuss the imager and present data and results from a recent field test.
A novel dual-band hyperspectral imaging system has been used to collect field test data for robotics vision applications. The imaging system can collect full scene hyperspectral images in both the long wave infrared (LWIR) band (8-10.5 μm) and the mid-wave infrare (MWIR) band (4-5.25 μm) simultaneously. The imager uses a specially designed Ge diffractive lens with a dual-band 320×240 HgCdTe infrared focal plane array (FPA) cooled with a Sterling cooler. The stacked FPA consists of two layers: the top one sensitive in the MWIR region and the bottom one sensitive in the LWIR region. The diffractive lens is designed to focus a first order, single-color (i.e., 8.0 μm) image in the LWIR onto the bottom layer of the FPA while at the same time focusing a second order single-color (i.e., 4.0 μm) image in the MWIR onto the top layer of the FPA. Images at different wavelengths are acquired by moving the lens along its optical axis. Moving the lens over the entire range during data collection allows sequential collection of spectral images in each band resulting in the collection of two complete image cubes. The focal length of the lens is 75 mm at 9 μm. The spectral resolution of the imager is 0.1 μm at the 9 μm wavelength. In general, 128 narrow wavelength bands are collected in each of the two broad spectral regions. After data collection, the images are processed to remove noise, contributions from unfocused wavelengths, and magnification differences. A description of the imager, data collection, noise removal, post-processing, and analysis are presented.
The maturation in the state-of-the-art of optical components is enabling increased applications for the technology. Most notable is the ever-expanding market for fiber optic data and communications links, familiar in both commercial and military markets. The inherent properties of optics and photonics, however, have suggested that components and processors may be designed that offer advantages over more commonly considered digital approaches for a variety of airborne sensor and signal processing applications. Various academic, industrial, and governmental research groups have been actively investigating and exploiting these properties of high bandwidth, large degree of parallelism in computation (e.g., processing in parallel over a two-dimensional field), and interconnectivity, and have succeeded in advancing the technology to the stage of systems demonstration. Such advantages as computational throughput and low operating power consumption are highly attractive for many computationally intensive problems. This review covers the key devices necessary for optical signal and image processors, some of the system application demonstration programs currently in progress, and active research directions for the implementation of next-generation architectures.
The relationships between lower and higher order cumulants of deterministic and random continuous signals are presented. This theory is used to develop time- and frequency-domain algorithms for the estimation of correlations (spectra) from triple correlations (bispectra) using acousto-optic processors. Cumulants are of interest because they are insensitive to a wide class of additive noises, including Gaussian noise of unknown covariance. Thus, noise-insensitive correlation (spectrum) estimates can be derived from higher order correlations (polyspectra). The potential for noise insensitivity is examined through the variance of power spectrum estimates based on conventional (second-order) and bispectrum statistics. A proof-of-principle experiment was carried out using an acousto-optic four-product processor to estimate the autocorrelation of a wideband periodic signal. The experimental data are compared with a simulation to validate the results.
KEYWORDS: Signal processing, Sensors, High dynamic range imaging, Charge-coupled devices, Signal detection, Optical signal processing, Radar signal processing, Diffusion, Diamond, Acousto-optics
The use of acousto-optic processors in applications such as radar signal processing and spectrum analysis places high performance requirements on the optical detectors used to detect the processed signal. In many signal processing applications there is a need for high speed, high dynamic range detectors with commensurate crosstalk and image lag performance. A program supported by Harry Diamond Laboratories for improved performance of CCD detectors has been highly successful in reducing crosstalk and obtaining over 40 db of optical detection dynamic range in the zero image lag mode of operation. Two different types of epi- layer structures, P on P+ and P on N, have been fabricated for which the methods and design issues to achieve high dynamic range, low crosstalk, low image lag, and high speed are discussed. The design approach used to suppress blooming is also discussed and the results of recent devices testing are shown.
Nonparametric estimation of lower-order statistics from higher-order statistics of continuous processes is considered. Of particular interest is estimation of correlations and spectra (second-order statistics) from higher-order correlations and polyspectra (higher-order statistics). The use of higher-order statistics is motivated by their insensitivity to a wide class of additive noises including Gaussian noise of unknown covariance. The fact that lower-order correlations are projections of higher-order correlations is exploited. Experimental results are presented using an acousto-optic triple product processor to estimate the autocorrelation of a 1- D signal.
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