The long-wave infrared (LWIR) hyperpectral sensing modality is one that is often used for the problem
of detection and identification of chemical warfare agents (CWA) which apply to both military
and civilian situations. The inherent nature and complexity of background clutter dictates a need
for sophisticated and robust statistical models which are then used in the design of optimum signal
processing algorithms that then provide the best exploitation of hyperspectral data to ultimately make
decisions on the absence or presence of potentially harmful CWAs. This paper describes the basic
elements of an automated signal processing pipeline developed at MIT Lincoln Laboratory. In addition
to describing this signal processing architecture in detail, we briefly describe the key signal models
that form the foundation of these algorithms as well as some spatial processing techniques used for
false alarm mitigation. Finally, we apply this processing pipeline to real data measured by the Telops
FIRST hyperspectral (FIRST) sensor to demonstrate its practical utility for the user community.
Proc. SPIE. 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
KEYWORDS: Target detection, Hyperspectral imaging, Statistical analysis, Detection and tracking algorithms, Data modeling, Sensors, Space operations, Hyperspectral target detection, Mahalanobis distance, RGB color model
One of the primary motivations for statistical LWIR background characterization studies is to support
the design, evaluation, and implementation of algorithms for the detection of various types of ground
targets. Typically, detection is accomplished by comparing the detection statistic for each test pixel
to a threshold. If the statistic exceeds the threshold, a potential target is declared. The threshold is
usually selected to achieve a given probability of false alarm. In addition, in surveillance applications,
it is almost always required that the system will maintain a constant false alarm rate (CFAR) as
the background distribution changes. This objective is usually accomplished by adaptively estimating
the background statistics and adjusting the threshold accordingly. In this paper we propose and
study CFAR threshold selection techniques, based on tail extrapolation, for a detector operating on
hyperspectral imaging data. The basic idea is to obtain reliable estimates of the background statistics
at low false alarm rates, and then extend these estimates beyond the range supported by the data to
predict the thresholds at lower false alarm rates. The proposed techniques are based on the assumption
that the distribution in the tail region of the detection statistics is accurately characterized by a member
of the extreme value distributions. We focus on the generalized Pareto distribution. The evaluation of
the proposed techniques will be done with both simulated data and real hyperspectral imaging data
collected using the Army Night Vision Laboratory COMPASS sensor.
Remote detection of chemical vapors in the atmosphere has a wide range of civilian and military
applications. In the past few years there has been significant interest in the detection of effluent
plumes using hyperspectral imaging spectroscopy in the 8-13&mgr;m atmospheric window. A major obstacle
in the full exploitation of this technology is the fact that everything in the infrared is a source of
radiation. As a result, the emission from the gases of interest is always mixed with emission by the
more abundant atmospheric constituents and by other objects in the sensor field of view. The radiance
fluctuations in this background emission constitute an additional source of interference which is much
stronger than the detector noise. In this paper we develop and evaluate parametric models for the
statistical characterization of LWIR hyperspectral backgrounds. We consider models based on the
theory of elliptically contoured distributions. Both models can handle heavy tails, which is a key
stastical feature of hyperspectral imaging backgrounds. The paper provides a concise description of
the underlying models, the algorithms used to estimate their parameters from the background spectral
measurements, and the use of the developed models in the design and evaluation of chemical warfare
agent detection algorithms.
The objective of this paper is to investigate the effects of dimensionality reduction on the statistical distribution of natural hyperspectral backgrounds. The statistical modeling is based on application of the multivariate t-elliptically contoured distribution to background regions which have been shown to exhibit "long-tail" behavior. Hyperspectral backgrounds are commonly represented with reduced dimensionality in order to minimize statistical redundancies in the spectral dimension and to satisfy data processing and storage requirements. In this investigation, we extend the statistical characterization of these backgrounds by modeling their Mahalanobis distance distributions in reduced dimensional space. The dimensionality reduction techniques applied in this paper include Principal Components Analysis (PCA) and spectral band aggregation. The knowledge gained from a better understanding of the effects of dimensionality reduction will be beneficial toward improving threshold selection for target detection applications. These investigations are done using hyperspectral data from the AVIRIS sensor and include spectrally homogeneous regions of interest obtained by visual interactive spatial segmentation.
Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms is to determine if and when a CWA enters the FOV of the sensor.
We shall first develop and evaluate algorithms for staring sensors following two different approaches. First, we will assume that no threat information is available and we design an adaptive anomaly detection algorithm to detect a statistically-significant change in the observed spectrum. The algorithm processes the observed spectra sequentially-in-time, estimates adaptively the background, and checks whether the next spectrum differs significantly from the background based on the Mahalanobis distance or the distance from the background subspace. In the second approach, we will assume that we know the spectral signature of the CWA and develop sequential-in-time adaptive matched filter detectors. In both cases, we assume that the sensor starts its operation before the release of the CWA; otherwise, staring at a nearby CWA-free area is required for background estimation. Experimental evaluation and comparison of the proposed algorithms is accomplished using data from a long-wave infrared (LWIR) Fourier transform spectrometer.
The objective of this paper is the statistical characterization of
natural hyperspectral backgrounds using the multivariate t-elliptically contoured distribution. Traditionally, hyperspectral backgrounds have been modeled using multivariate Gaussian distributions; however it is well known that real data often exhibit "long-tail" behavior that cannot be accounted by normal distribution models. The proposed multivariate t-distribution model has elliptical equiprobability contours whose center and ellipticity is specified by the mean vector and covariance matrix of the data. The density of the contours, which is reflected into the distribution of the Mahalanobis distance, is controlled by an extra parameter, the number of degrees of freedom. As the number of degrees of freedom increases, the tails decrease and approach those of a normal distribution with the same mean and covariance. In this work we investigate the application of t-elliptically contoured distributions to the characterization of different hyperspectral background data obtained by visually interactive spatial segmentation ("physically" homogeneous classes), automated clustering algorithms using spectral similarity metrics (spectrally
homogeneous classes), and by fitting normal mixture models (statistically homogeneous classes). These investigations are done
using hyperspectral data from the AVIRIS sensor.
Riverside Research Institute (RRI) performed a three-month study of the sensor physics which will be useful in an urban warfare environment for the Army DCS G-2. Different phenomena were qualitatively evaluated for their utility in providing improved situational awareness and identification of specific threats and obstacles present within a city while supporting the warfighter applicable to Intelligence, Surveillance and Reconnaissance (ISR). The goal was to provide a technology investment strategy to maximize the benefit of future Measurement and Signature Intelligence (MASINT) Science and Technical Intelligence (S&IT) systems. This report summarizes the findings of the study. No single sensor program or phenomenology addresses the broad spectrum of war fighter needs within the challenging urban terrain. Understanding the user community requirements is a necessary next step in quantifying and prioritizing MASINT performance parameters to provide the FF (Future Force) a decisive advantage in urban areas.
In order to better understand the interaction of laser light with biological tissue, a light-transport model is integrated with a heat-transport model. The outputs include temperature as a function of position and time, given the illumination conditions and the optical and thermal properties of the tissue. The optical portion of the algorithm is based on the theory of radiative transfer through a turbid medium. Our computer program models multiple scattering in three dimensions using seven discrete irradiances which approximate the radiative transport equation. The distribution of absorbed light in the tissue is calculated and used as the source term in a discrete approximation to the thermal diffusion equation. Recently, we have been using the model to better understand the laser-heating of heterogeneous tissue. Rather than modeling a homogeneous mixture having properties given by weighted averages of those of tissue and blood, we model this medium as an array of blood vessels in a bloodless dermis background. We are currently analyzing temporal and spatial variations of temperature in homogeneous and heterogeneous tissue having identical blood concentrations. A particular application of the model is to the study of laser coagulation tonsillectomy.