Adaptive techniques for detecting small or difficult targets in the midst of high noise and/or clutter have a rich history in the radar array processing community. However, the utility of these schemes is only beginning to be realized for multichannel electro-optical techniques, specifically hyperspectral imaging (HSI). The data products generated by hyperspectral sensors differ greatly from those of radar and sonar arrays, yet recent studies using HSI data have offered promising results for modified versions of common adaptive detectors. In this paper, we compare a series popular adaptive detection schemes applied to HSI data for the task of land mine detection. Experiments using real hyperspectral image cubes, not simulations, are performed with data from both the visible-SWIR and LWIR regions. Results are presented for different mine types in a variety of scenes.
Proc. SPIE. 5890, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: Numerical Atmospheric Prediction and Environmental Monitoring
KEYWORDS: Target detection, Long wavelength infrared, Signal to noise ratio, Hyperspectral imaging, Principal component analysis, Sensors, Remote sensing, Gases, Hyperspectral target detection, RGB color model
For the past ten years, much of the research in hyperspectral image data exploitation techniques has been focused on detection of ground targets. As a passive remote sensing technique, hyperspectral imagers have performed reasonably well in detecting the presence of a variety of objects; from crop species to land mines to mineral deposits to vehicles under camouflage. These often promising results have prompted new studies of hyperspectral remote sensing for other applications - including atmospheric monitoring. Should technologies like hyperspectral imaging prove effective in emission source monitoring, organizations interested in environmental assessment could transition from inspection using hand-held analytical instruments to a truly standoff technique. In this paper, we evaluate the utility of a set of hyperspectral exploitation techniques applied to the task of gas detection. This set of techniques is a sampling of approaches that have appeared in the literature, and all of the methods discussed have demonstrated utility in the reflective regime. Specifically, we look at signature-based detection, anomaly detection, transformations (i.e. rotations) of the spectral space, and even dedicated band combinations and scatter plots. Using real LWIR hyperspectral data recently collected on behalf of the US Environmental Protection Agency, we compare performance in detecting three different industrial gases.
This paper addresses the utility of robust automatic clustering of
hyperspectral image data. Such clustering is possible only when
the background in a scene is accurately modeled. Mixtures of
non-Gaussian densities have been discussed recently, and here we
move further down this path. We derive a t mixture model for
the background in hyperspectral images, using two techniques for
estimating parameters based on the Expectation-Maximization
algorithm. Visual and statistical evaluation of these techniques
are made with AVIRIS data. Dealing with the data's inhomogeneity
by developing proper models of the background (i.e. clutter) in a
hyperspectral image is important in target detection applications,
especially for accurate performance prediction and detector