Most hyperspectral chemical gaseous plume quantification algorithms assume a priori knowledge of the plume
temperature either through direct measurement or an auxiliary temperature estimation approach. In this paper,
we propose a new quantification algorithm that can simultaneously estimate the plume strength as well as its
temperature. We impose only a mild spatial assumption, that at least one nearby pixel shares the same plume
parameters as the target pixel, which we believe will be generally satisfied in practice. Simulations show that
the performance loss incurred by estimating both the temperature and plume strength is small, as compared to
the case when the plume temperature is known exactly.
We propose an algorithm for standoff quantification of chemical vapor plumes from hyperspectral imagery. The approach is based on the observation that the quantification problem can be easily solved in each pixel with the use of just a single spectral band if the radiance of the pixel in the absence of the plume is known. This plume-absent radiance may, in turn, be recovered from the radiance of the subset of spectral bands in which the gas species is transparent. This “selected-band” algorithm is most effective when applied to gases with narrow spectral features, and are therefore transparent over many bands. We also demonstrate an iterative version that expands the range of applicability. Simulations show that the new algorithm attains the accuracy of existing nonlinear algorithms, while its computational efficiency is comparable to that of linear algorithms.
Existing chemical plume quantification algorithms assume that the off-plume radiance of a pixel containing the plume signal is unobservable. When the problem is limited to a single gas, the off-plume radiance may be estimated from the bands in which the gas absorption is nearly zero. It is then possible to compute the difference between the on- and off-plume radiances and solve for the plume strength from Beer's Law. The major advantage of this proposed method is that the gas strength can be resolved from the radiance difference so that the estimation error remains small for thick plumes.
Most chemical gas detection algorithms for long-wave infrared hyperspectral images assume a gas with a perfectly known spectral signature. In practice, the chemical signature is either imperfectly measured and/or exhibits spectral variability due to temperature variations and Beers law. The performance of these detection algorithms degrades further as a result of unavoidable contamination of the background covariance by the plume signal. The objective of this work is to explore robust matched filters that take the uncertainty and/or variability of the target signatures into account and mitigate performance loss resulting from different factors. We introduce various techniques that control the selectivity of the matched filter and we evaluate their performance in standoff LWIR hyperspectral chemical gas detection applications.
The passive remote chemical plume quantication problem may be approached from multiple aspects, corresponding
to a variety of physical eects that may be exploited. Accordingly, a diversity of statistical quantication
algorithms has been proposed in the literature. The ultimate performance and algorithmic complexity of each is
uenced by the assumptions made about the scene, which may include the presence of ancillary measurements
or particular background / plume features that may or may not be present. In this paper, we evaluate and
compare a number of quantication algorithms that span a variety of such assumptions.
Most chemical gas detection algorithms for hyperspectral imaging applications assume a gas with a perfectly
known spectral signature. In practice, the chemical signature is either imperfectly measured and/or exhibits
spectral variability due to temperature variations and Beer's law. The objective of this work is to explore robust
matched filters that take the uncertainty and/or variability of the target signatures into account. We introduce
various techniques that control the selectivity of the matched filter and we evaluate their performance in standoff
LWIR hyperspectral chemical gas detection applications.
The detection of gaseous chemical plumes in long-wave infrared hyperspectral images is often accomplished
with algorithms derived from linear radiance models, such as the matched filter. While such algorithms can be
highly effective, deviations of the physical radiative transfer process from the idealized linear model can reduce
performance. In particular, the steering vector employed in the matched filter will never exactly match the
observed plume signature, the estimated background covariance matrix will often suffer some contamination
by the plume signature, and the plume and background will typically be spatially correlated to some extent.
In combination, these effects can be worse than they are individually. In this paper, we systematically vary
these factors to study their impact on detection using a data set of synthetic plumes embedded into measured
Accurate statistical models for hyperspectral imaging (HSI) data distribution are useful for many applications.
A family of elliptically contoured distribution (ECD) has been investigated to model the unimodal ground cover
classes. In this paper we propose to test the elliptical symmetry of real unimodal HSI clutters which will answer
the question whether the family of ECD will provide an appropriate model for HSI data. We emphasize that the
elliptical symmetry is an inherent feature shared by all ECDs. It is a prerequisite that real HSI clutters must
pass these elliptical symmetry tests, so that the family of ECD can be qualified to model these data accurately.