Hyperspectral imaging is particular useful in remote sensing to identify a small number of unknown man-made
objects in a large natural background. An algorithm for detecting such anomalies in hyperspectral imagery is
developed in this article. The pixel from a data cube is modeled as the sum of a linear combination of unknown
random variables from the clutter subspace and a residual. Maximum likelihood estimation is used to estimate
the coecients of the linear combination and covariance matrix of the residual. The Mahalanobis distance of
the residual is dened as the anomaly detector. Experimental results obtained using a hyperspectral data cube
with wavelengths in the visible and near-infrared range are presented.
The Photonics Research Center at the United States Military Academy is conducting research to demonstrate the
feasibility of combining hyperspectral imaging and Raman spectroscopy for remote chemical detection over a broad area
of interest. One limitation of future trace detection systems is their ability to analyze large areas of view. Hyperspectral
imaging provides a balance between fast spectral analysis and scanning area. Integration of a hyperspectral system
capable of remote chemical detection will greatly enhance our soldiers' ability to see the battlefield to make threat
related decisions. It can also queue the trace detection systems onto the correct interrogation area saving time and
reconnaissance/surveillance resources. This research develops both the sensor design and the detection/discrimination
algorithms. The one meter remote detection without background radiation is a simple proof of concept.
Anomaly detection for hyperspectral imaging is typically based on the Mahalanobis distance. The sample statistics for Mahalanobis distance are not resistant to the anomalies that are present in the sample pixels. Consequently, the sample statistics do not estimate the corresponding population parameters accurately. In this paper, we will present an algorithm for hyperspectral anomaly detection based on the Mahalanobis distance computed using robust statistics which are estimated based on the minimum generalized variance of the sample pixels. Numerical results based on actual hyperspectral images will be presented.
A readily automated procedure for testing and calibrating the wavelength scale of a scanning hyperspectral imaging camera is described. The procedure is a laboratory calibration method and it uses the absorbance features from a commercial didymium oxide filter as a wavelength standard. The procedure was used to accurately determine the pixel positions. An algorithm was developed to determine the center of the wavelength for any given abscissa accurately. During this investigation we determined that the sampled pixels show both trend and serial correlation as a function of the spatial dimensions. The trend is more significant than the serial correlation. In this paper, the trend will be filtered out by modeling the trend using an efficient global linear regression model of different order for different spectral band. The order is selected automatically and different criteria for selecting the order are discussed. Experimental results will be discussed.
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