Edisanter Lo

Hyperspectral anomaly detection based on minimum generalized variance method

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 hyperspectral anomaly detector based on partialing out a clutter subspace

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.

Algorithm for Detecting Anomaly in Hyperspectral Imagery Using Factor Analysis

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.

Hyperspectral outlier detector based on conditional distributions

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.

A hyperspectral anomaly detector based on partitioning pixel into adjacent components

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.

Hyperspectral anomaly detection based on variable dimension for clutter subspace

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.

Transformation for target detection in hyperspectral imaging

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 in hyperspectral Imaging using logistic regression

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.

Wavelength calibration of hyperspectral sensors with 2D detector arrays

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.