Sidi Niu

Algorithms for remote quantification of chemical plumes: a comparative study

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 in 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.

Design and evaluation of robust matched filters for chemical agent detection

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 Beers 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.

Implications of model mismatch and covariance contamination on chemical detection algorithms

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 background data.

On the modeling of hyperspectral imaging data with elliptically contoured distributions

Accurate statistical models for hyperspectral imaging (HSI) data are fundamental for many subsequent applications including detection, classification, and estimation. Suppose the whole nonhomogeneous HSI data is well classified into homogeneous unimodal clutters, we find that the family of elliptically contoured distributions (ECDs) is capable of providing sufficiently accurate model for each clutter. In this paper, several techniques are applied to test the elliptical symmetry of HSI clutters. Instead of testing elliptical symmetry directly, its counterpart spherical symmetry is examined for the whitened unimodal clutters. For each clutter which passes these symmetry checking tests, fitting an appropriate ECD based model to the data can be done in the Mahalanobis distance direction.

Hyperspectral chemical plume quantification via background radiance estimation

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 Beers 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.

Hyperspectral chemical plume quantification and temperature estimation

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.