Passive imaging of concealed objects at stand-off distances in excess of a few meters requires both excellent spatial,
thermal and temporal resolution from the terahertz imaging system. The combination of these requirements while
keeping the overall system cost at a reasonable level has been the motivation for this joint work. The THz imaging
system under development is capable of sub-Kelvin NETD at video frame rates. In this paper we report the first imaging
results from a 16-pixel array of superconducting antenna-coupled NbN vacuum-bridge microbolometers, operated within
a cryogen-free, turn-key refrigerator. In addition to the system overview, we shall also address the uniformity of the
detectors and present passive indoors raster-scanned imagery.
We present ultrawideband imagery obtained with modular, 8-element, superconducting Nb microbolometer arrays.
Conically scanned images are presented and compared with
raster-scanned images obtained on the same arrays and
from similar NbN arrays at VTT. Statistical data on detector
non-uniformity, and methods for mitigating and
compensating it are described. Low-noise readout is accomplished with room-temperature electronics using the
transimpedance scheme of Pentilla et al. Characterization of spatial resolution, noise-equivalent temperature
difference, and spectral response is done using metrology
tools - standard targets, mm-wave blackbodies, and variable
filters - that have been developed at NIST for this purpose.
This work presents the application of a basic unsupervised classification algorithm for the segmentation of indoor passive
Terahertz images. The 30,000 pixel broadband images of a person with concealed weapons under clothing are taken
at a range of 0.8-2m over a frequency range of 0.1-1.2THz using single-pixel row-based raster scanning. The spiral-antenna
coupled 36x1x0.02&mgr;m Nb bridge cryogenic micro-bolometers are developed at NIST-Optoelectronics Division.
The antenna is evaporated on a 250&mgr;m thick Si substrate with a 4mm diameter hyper-hemispherical Si lens. The NETD
of the microbolometer is 125mK at an integration time of 30 ms. The background temperature calibration is performed
with a known 25 pixel source above 330 K, and a measured background fluctuation of 200-500mK. Several weapons
were concealed under different fabrics: cotton, polyester, windblocker jacket and thermal sweater. Measured temperature
contrasts ranged from 0.5-1K for wrinkles in clothing to 5K for a zipper and 8K for the concealed weapon. In order to
automate feature detection in the images, some image processing and pattern recognition techniques have been applied
and the results are presented here. We show that even simple algorithms, that can potentially be performed in real time,
are capable of differentiating between a metal and a dielectric object concealed under clothing. Additionally, we show that
pre-processing can reveal low temperature contrast features, such as folds in clothing.
The main challenge for the retrieval of information using hyperspectral sensors is that due to the high dimensionality provided by them there is not comparably enough a priori data to produce well-estimated parameters to solve our detection problem. This lack of enough a priori information for an estimation yields to a rank-deficient problem. As a consequence, this leads to an increment in false alarms and increase in the probability of missing throughout the classification process. An approach based on a regularization technique applied to the data collected from the hyperspectral sensor is used to simultaneously minimize the probabilities of false alarms and missing. This procedure is implemented using algorithms that apply regularization techniques by biasing the covariance matrix, which enable the simultaneous reduction of the probability of false alarm and the decrease of the probability of missing; thus, enhancing the Maximum Likelihood parameter estimation.
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