Gas-phase chemical plumes exhibit, particularly in the infrared, distinctive emission signatures as a function of wavelength. Hyperspectral imagery can exploit this distinctiveness to detect specific chemicals, even at low concentrations, using matched filters that are tailored both the the specific structure of the chemical signature and to the statistics of the background clutter. But what if the chemical species is unknown? One can apply matched filters to a long list of candidate chemicals (or chemical mixtures), or one can treat the problem as one of anomaly detection. In this case, however, the anomalous signals of interest are not completely unknown. Gas spectra are generically sparse (absorbing or emitting at only a few wavelengths), and this property can be exploited to enhance the sensitivity of anomaly detection algorithms. This paper investigates the utility of sparse signal anomaly detection for the problem of finding plumes of gas with unknown chemistry in hyperspectral imagery.
The goal of anomalous change detection (ACD) is to identify what unusual changes have occurred in a scene,
based on two images of the scene taken at different times and under different conditions. The actual anomalous
changes need to be distinguished from the incidental differences that occur throughout the imagery, and one of
the most common and confounding of these incidental differences is due to the misregistration of the images,
due to limitations of the registration pre-processing applied to the image pair.
We propose a general method to compensate for residual misregistration in any ACD algorithm which constructs
an estimate of the degree of "anomalousness" for every pixel in the image pair. The method computes
a modified misregistration-insensitive anomalousness by making local re-registration adjustments to minimize
the local anomalousness. In this paper we describe a symmetrized version of our initial algorithm, and find
significant performance improvements in the anomalous change detection ROC curves for a number of real and
synthetic data sets.
We describe the development of a simulation framework for anomalous change detection that considers both the
spatial and spectral aspects of the imagery. A purely spectral framework has previously been introduced, but
the extension to spatio-spectral requires attention to a variety of new issues, and requires more careful modeling
of the anomalous changes. Using this extended framework, we evaluate the utility of spatial image processing
operators to enhance change detection sensitivity in (simulated) remote sensing imagery.
Template matching is the search for a known object, represented by a template image, at an arbitrary location within a larger image. The local measure of match is often desired to be invariant to certain transforms, such as rotation and dilation, of the template. Although a variety of solutions have been proposed, most are designed to provide invariance to a specific transform or set of transforms, and often involve significant computational demands. When invariance to “small” transformations of the template (e.g., rotation by a small angle) is sufficient, local linear approximations to these transforms may be used to allow template matching with invariance to arbitrary transforms, without significantly increased computational requirements.
A new set of boundary-handling algorithms has been developed for discrete wavelet transforms in the ISO/IEC JPEG-2000 Still Image Coding Standard. Two polyphase component extrapolation policies are
specfied: a constant extension policy and a symmetric extension policy. Neither policy requires any computations to generate the extrapolation. The constant extension policy is a low-complexity option that buffers just one sample from each end of the input being extrapolated. The symmetric extension policy has slightly higher memory and conditional-logic requirements but is mathematically equivalent to wholesample symmetric pre-extension when used with whole-sample symmetric filter banks. Both policies can be
employed with arbitrary lifted filter banks, and both policies preserve resolution scalability and reversibility. These extension policies will appear in Annex H, "Transformation of images using arbitrary wavelet transformations," in Part 2 ("Extensions") of the JPEG-2000 standard.
The recent JPEG2000 image coding standard includes a lossless coding mode based on reversible integer to integer filter banks, which are constructed by inserting rounding operations into the filter bank lifting factorisation. The baseline (Part 1) of the JPEG2000 standard supports a single reversible filter bank, the finite length input to which is symmetrically extended to avoid difficulties at the boundaries. While designing support for arbitrary filter banks for Part 2 of the standard, we discovered that reversibility is not always possible for even length integer to integer filter banks combined with symmetric pre-extension.