In this paper an end-to-end hyperspectral imaging system model is described which has the ability to predict the
performance of hyperspectral imaging sensors in the visible through to the short-wave infrared regime for sub-pixel
targets. The model represents all aspects of the system including the target signature and background, the atmosphere,
the optical and electronic properties of the imaging spectrometer, as well as details of the processing algorithms
employed. It provides an efficient means of Monte-Carlo modelling for sensitivity analysis of model parameters over a
wide range. It is also capable of representing certain types of
non-Gaussian hyperspectral clutter arising from
heterogeneous backgrounds. The capabilities of the model are demonstrated in this paper by considering Uninhabited
Airborne Vehicle scenarios and comparing both multispectral and hyperspectral sensors. Both anomaly detection and
spectral matched-filter algorithms are characterised in terms of Receiver Operating Characteristic curves. Finally, some
results from a preliminary validation exercise are presented.
Techniques to determine the proportions of constituent materials within a single pixel spectrum are well documented in the reflective (0.4-2.5μm) domain. The same capability is also desirable for the thermal (7-14μm) domain, but is complicated by the thermal contributions to the measured spectral radiance. Atmospheric compensation schemes for the thermal domain have been described along with methods for estimating the spectral emissivity from a spectral radiance measurement and hence the next stage to be tackled is the unmixing of thermal spectral signatures. In order to pursue this goal it is necessary to collect data of well-calibrated targets which will expose the limits of the available techniques and enable more robust methods to be designed. This paper describes the design of a set of ground targets for an airborne hyperspectral imager, which will test the effectiveness of available methods. The set of targets include panels to explore a number of difficult scenarios such as isothermal (different materials at identical temperature), isochromal (identical materials, but at differing temperatures), thermal adjacency and thermal point sources. Practical fabrication issues for heated targets and selection of appropriate materials are described. Mathematical modelling of the experiments has enabled prediction of at-sensor measured radiances which are used to assess the design parameters. Finally, a number of useful lessons learned during the fielding of these actual targets are presented to assist those planning future trials of thermal hyperspectral sensors.
This paper explores three related themes: the statistical nature of hyperspectral background clutter; why should it be like this; and how to exploit it in algorithms. We begin by reviewing the evidence for the non-Gaussian and in particular fat-tailed nature of hyperspectral background distributions. Following this we develop a simple statistical model that gives some insight into why the observed fat tails occur. We demonstrate that this model fits the background data for some hyperspectral data sets. Finally we make use of the model to develop hyperspectral detection algorithms and compare them to traditional algorithms on some real world data sets.
Hyperspectral imaging provides the potential to derive sub-pixel material abundances. This has significant utility in the detection of sub-pixel targets or targets concealed under canopy. The linear mixture model describes spectral data in terms of a basis set of pure material spectra or endmembers. The success of such a model is dependent on the choice and number of endmembers used and the unmixing process. Endmember spectra may come from field or laboratory measurements, however, differences between sensors and changes in environmental conditions may mean that the measurement is not representative of the material as found in the scene. Alternatively, a number of algorithms exist to select spectra from the data directly, but these assume pure examples of the complete set of materials exist within the imagery. In either case, the chosen set of endmembers may not optimally describe the data in a linear mixing sense. In this paper some new methods for endmember selection are presented. These are evaluated on hyperspectral imagery and the results compared with those of a well-known automatic selection technique. Finally, an improved unmixing architecture is proposed which is self-consistent in terms of endmember selection and the unmixing process.
KEYWORDS: Detection and tracking algorithms, Statistical analysis, Target detection, Hyperspectral imaging, Statistical modeling, Data modeling, Data analysis, Image processing, Monte Carlo methods, Sensors
Anomaly detection in hyperspectral imagery is a potentially powerful approach for detecting objects of military interest because it does not require atmospheric compensation or target signature libraries. A number of methods have been proposed in the literature, most of these require a parametric model of the background probability distribution to be estimated from the data. There are two potential difficulties with this. First a parametric model must be postulated which is capable of describing the background statistics to an adequate approximation. Most work has made use of the multivariate normal distribution. Secondly the parameters must be estimated sufficiently accurately - this can be problematic for the covariance matrix of high dimensional hyperspectral data. In this paper we present an alternative view and investigate the capabilities of anomaly detection algorithms starting from a minimal set of assumptions. In particular we only require the background pixels to be samples from an independent and identically distributed (iid) process, but do not require the construction of a model for this distribution. We investigate a number of simple measures of the 'strangeness' of a given pixel spectra with respect to the observed background. An algorithm is proposed for detecting anomalies in a self-consistent way. The effectiveness of the algorithms is compared with a well-known anomaly detection algorithm from the literature on real hyperspectral data sets.
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