Conventional algorithms for target detection in hyperspectral imaging usually require multivariate normal distributions for the background and target pixels. Significant deviation from the assumed distributions could lead to incorrect detection. It is possible to make the non-normal pixels into more normal-looking pixels by using a transformation on the pixels. A multivariate transformation based maximum likelihood is proposed in this paper to improve target detection in hyperspectral imaging. Experimental results show that the distribution of the transformed pixels become closer to a multivariate normal distribution and the performance of the detection algorithms improves after the transformation.
Target detection is an important application in hyperspectral imaging. Conventional algorithms for target detection assume that the pixels have a multivariate normal distribution. The pixels in most images do not have multivariate normal distributions. The logistic regression model, which does not require the assumption of multivariate normal distribution, is proposed in this paper as a target detection algorithm. Experimental results show that the logistic regression model can work well in target detection.
We develop an algorithm based on a subspace model to detect anomalies in a hyperspectral image. The anomaly detector is based on the Mahalanobis distance of a residual from a pixel that is partitioned nonuniformly according to the groups in the spectral components in the pixel. The main background is removed from the pixel by predicting linear combinations of each subset of the partitioned pixel with linear combinations of the main background. The residual is defined to be the difference between the linear combinations of each subset of the partitioned pixel and the linear combinations of the main background. The anomaly detector is designed for anomalies that can be best detected in the residual of the pixel. Experimental results using two real hyperspectral images and a simulated dataset show that the anomaly detector outperforms conventional anomaly detectors.
Detection of a subspace anomaly is an important application of hyperspectral imaging in remote sensing. Sub-space anomaly detection depends on the unknown dimension of the main background subspace. When the dimension is high, detection algorithms tend to have unsatisfactory performance. This paper proposes an anomaly detection algorithm that will continue to perform satisfactorily when the dimension is high.
Detection of anomalous objects in a large scene is an important application of hyperspectral imaging in remote sensing. Current algorithms for anomaly detection are based on partialling out the main background structure from each spectral component of a pixel from a hyperspectral image. The Maximized Subspace Model (MSM) detector has the best probability of detection in comparison with the other anomaly detectors that are based on this model. This paper proposes an anomaly detection algorithm that is based on a more general model than the MSM detector. The anomaly detector is also defined as the Mahalanobis distance of the resulting residual. Experimental results show that the anomaly detector has a substantial improvement in detection over the conventional anomaly detectors.
Anomaly detectors based on subspace models have the dimension of the clutter subspace as the parameter with
a large range of values. An anomaly detector that has a different parameter with fewer values is proposed.
The known pixel from a hyperspectral image is predicted with a linear transformation of the unknown variables
from the clutter subspace and the coefficients of the linear transformation are unknown. The dimension of the
clutter subspace can vary from one spectral component of the pixel to another. The anomaly detector is the
Mahalanobis distance of the error. The experimental results show that the parameter in the anomaly detector
has a significantly reduced number of possible values in comparison with the conventional anomaly detectors.
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.
An outlier detection algorithm for hyperspectral imaging based on likelihood ratio test is presented in this article.
The null hypothesis tests if a test pixel is from the conditional distribution of the pixel given the background
subspace and the alternative hypothesis tests if a test pixel is from the conditional distribution of the pixel given
the target subspace. Using principal components for the complementary subspaces, a practical outlier detector
is developed and is compared to conventional outlier detectors using a VNIR hyperspectral imagery.
An anomaly detector for hyperspectral imaging based on partialling out the effect of the clutter subspace is
devised. The partialling maximizes the squared correlation between each spectral component and a linear
predictor, with no restrictions on the form of the probability distribution. The detection step is defined by
thresholding a Mahalanobis measure of the prediction error. The method is compared to conventional anomaly
detectors using VNIR hyperspectral imagery.
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.
The wavelength of the spectral bands of a pixel from a hyperspectral imaging camera is not automatically known. A simple linear regression model can be fitted to the known abscissa and the corresponding known wavelength to acquire a calibration equation to determine the wavelength for any given abscissa. In our experiment the pixels show significant trend and serial correlation mainly in one of the spatial domain. An algorithm to remove the trend and serial correlation from the pixels using local linear regression model will be presented. Numerical results will be presented to show the improvement in the accuracy of the calibration equation computed using the corrected pixels.
An algorithm to determine the abscissa of the partial pixels that corresponds to the peaks of an absorbance spectrum from a hyperspectral imaging camera will be described. The algorithm is based on local linear regression models in variable order and variable sample size mode. The sample size is determined by using the estimated critical points and inflection points. The order is determined by statistically comparing the sum of squares error of the regression models for different orders. Numerical results on spectra from a hyperspectral cube will be presented.
A supervised subpixel target detection algorithm based on parametric general linear model using whitening transformation for hyperspectral imaging is developed. Statistical tests are described to assess the performance of the algorithm in comparison with the corresponding classical approach. Numerical results are presented to show that the parametric algorithm using low-order models can adequately represent the classical model.