Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images

Anomaly detection in hyperspectral images has proven valuable in many applications, such as hazardous material and mine detection. The benchmark anomaly detector is the Reed-Xiaoli (RX) detector, which is based on the local multivariate normality of background. The RX algorithm, along with its many modified versions, has been widely explored, and the main concerns identified are related to local background covariance matrix estimation. The small sample size, local background nonhomogeneity, and the presence of target pixels within the estimation window are factors that can deeply affect local background covariance matrix estimation. These critical aspects may occur together in the same operational scenario, and they may strongly impair the detection performance. However, due to their intrinsic difference, these aspects have been typically discussed within different frameworks, disregarding the possible existing connections while developing different approaches to solution. We investigate these critical aspects, along with their impact on the detection process, from an operational detection perspective. The approaches to solution are critically analyzed, discussing possible links and connections. Real hyperspectral data are employed for assessing if the algorithms, designed ad hoc to solve a specific problem, can either handle more complex situations, or bring about further complications.

[1]  David A. Landgrebe,et al.  Covariance Matrix Estimation and Classification With Limited Training Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  P. Rousseeuw.  Tutorial to robust statistics , 1991 .

[3]  John Ingram,et al.  Hyperspectral anomaly detection based on minimum generalized variance method , 2008, SPIE Defense + Commercial Sensing.

[4]  Avishai Ben-David,et al.  Performance loss of multivariate detection algorithms due to covariance estimation , 2009, Remote Sensing.

[5]  Dimitris G. Manolakis,et al.  Taxonomy of detection algorithms for hyperspectral imaging applications , 2005 .

[6]  Marco Diani,et al.  A New Algorithm for Robust Estimation of the Signal Subspace in Hyperspectral Images in the Presence of Rare Signal Components , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Mark J. Carlotto,et al.  A cluster-based approach for detecting man-made objects and changes in imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Tiziana Veracini,et al.  Fully Unsupervised Learning of Gaussian Mixtures for Anomaly Detection in Hyperspectral Imagery , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[9]  David Malah,et al.  Local-global background modeling for anomaly detection in hyperspectral images , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[10]  Andreas F. Hayden,et al.  Observations on the relationship between eigenvalues, instrument noise, and detection performance , 2002, SPIE Optics + Photonics.

[11]  Nasser M. Nasrabadi,et al.  Regularization for spectral matched filter and RX anomaly detector , 2008, SPIE Defense + Commercial Sensing.

[12]  Stefania Matteoli,et al.  Improved covariance matrix estimation: interpretation and experimental analysis of different approaches for anomaly detection applications , 2009, Remote Sensing.

[13]  Wojciech Pieczynski,et al.  SEM algorithm and unsupervised statistical segmentation of satellite images , 1993, IEEE Trans. Geosci. Remote. Sens..

[14]  John R. Schott,et al.  Comparative evaluation of background characterization techniques for hyperspectral unstructured matched filter target detection , 2007 .

[15]  I. Reed,et al.  Rapid Convergence Rate in Adaptive Arrays , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[17]  S Matteoli,et al.  A tutorial overview of anomaly detection in hyperspectral images , 2010, IEEE Aerospace and Electronic Systems Magazine.

[18]  P. Rousseeuw,et al.  A fast algorithm for the minimum covariance determinant estimator , 1999 .

[19]  S.R. Rotman,et al.  Improved covariance matrices for point target detection in hyperspectral data , 2008, 2009 IEEE International Conference on Microwaves, Communications, Antennas and Electronics Systems.

[20]  K.W. Bauer,et al.  Finding Hyperspectral Anomalies Using Multivariate Outlier Detection , 2007, 2007 IEEE Aerospace Conference.

[21]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.