Unsupervised image segmentation for passive THz broadband images for concealed weapon detection

摘要

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.