Simplex ACE: a constrained subspace detector

[1]  James Theiler,et al.  Local background estimation and the replacement target model , 2017, Defense + Security.

[2]  Timothy Doster,et al.  A parametric study of unsupervised anomaly detection performance in maritime imagery using manifold learning techniques , 2016, SPIE Defense + Security.

[3]  James Theiler,et al.  Graph-based and statistical approaches for detecting spectrally variable target materials , 2016, SPIE Defense + Security.

[4]  Alina Zare,et al.  Instance influence estimation for hyperspectral target signature characterization using extended functions of multiple instances , 2016, SPIE Defense + Security.

[5]  Amanda Ziemann,et al.  Local spectral unmixing for target detection , 2016, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

[6]  James Theiler,et al.  Right spectrum in the wrong place: a framework for local hyperspectral anomaly detection , 2016, Computational Imaging.

[7]  Emmett J. Ientilucci,et al.  Target detection assessment of the SHARE 2010/2012 hyperspectral data collection campaign , 2015, Defense + Security Symposium.

[8]  David W. Messinger,et al.  An adaptive locally linear embedding manifold learning approach for hyperspectral target detection , 2015, Defense + Security Symposium.

[9]  Yin-Fong Su,et al.  Quantitative reflectance spectra of solid powders as a function of particle size. , 2015, Applied optics.

[10]  Alan P. Schaum,et al.  Enough with the additive target model , 2014, Defense + Security Symposium.

[11]  Stefania Matteoli,et al.  An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Jon Atli Benediktsson,et al.  Unsupervised methods for the classification of hyperspectral images with low spatial resolution , 2013, Pattern Recognit..

[13]  David W. Messinger,et al.  The SHARE 2012 data campaign , 2013, Defense, Security, and Sensing.

[14]  John P. Kerekes,et al.  SpecTIR hyperspectral airborne Rochester experiment data collection campaign , 2012, Defense + Commercial Sensing.

[15]  Daniel Mandl,et al.  High-speed atmospheric correction for spectral image processing , 2012, Defense + Commercial Sensing.

[16]  William J. Emery,et al.  Very High Resolution Multiangle Urban Classification Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[17]  David W. Messinger,et al.  Metrics of spectral image complexity with application to large area search , 2012 .

[18]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  J. Jacobson,et al.  Hyperspectral detection and discrimination using the ACE algorithm , 2011, Optical Engineering + Applications.

[20]  Stefania Matteoli,et al.  Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Antonio J. Plaza,et al.  Survey of geometric and statistical unmixing algorithms for hyperspectral images , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[22]  Peter Bajorski,et al.  Hyperspectral target detection in a whitened space utilizing forward modeling concepts , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[23]  David W. Messinger,et al.  Iterative convex hull volume estimation in hyperspectral imagery for change detection , 2010, Defense + Commercial Sensing.

[24]  Alan Schaum,et al.  Continuum fusion: a theory of inference, with applications to hyperspectral detection. , 2010, Optics express.

[25]  James Theiler,et al.  Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery , 2010, IEEE Geoscience and Remote Sensing Letters.

[26]  C. Gittins,et al.  Detection and characterization of chemical vapor fugitive emissions by nonlinear optimal estimation: theory and simulation. , 2009, Applied optics.

[27]  John P. Kerekes,et al.  Development of a Web-Based Application to Evaluate Target Finding Algorithms , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Steven M. Adler-Golden,et al.  Hyperspectral Detection and Identification with Constrained Target Subspaces , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[29]  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.

[30]  Rama Chellappa,et al.  Hybrid Detectors for Subpixel Targets , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  M.T. Eismann,et al.  Strategies for hyperspectral target detection in complex background environments , 2006, 2006 IEEE Aerospace Conference.

[32]  A. Fraser,et al.  Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery , 2005 .

[33]  Louis L. Scharf,et al.  The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic , 2005, IEEE Transactions on Signal Processing.

[34]  Rama Chellappa,et al.  A hybrid algorithm for subpixel detection in hyperspectral imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[35]  Bea Thai,et al.  Invariant subpixel material detection in hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[36]  Alan P. Schaum,et al.  Spectral subspace matched filtering , 2001, SPIE Defense + Commercial Sensing.

[37]  L.L. Scharf,et al.  Adaptive matched subspace detectors and adaptive coherence estimators , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

[38]  A. Hayden,et al.  Determination of trace-gas amounts in plumes by the use of orthogonal digital filtering of thermal-emission spectra. , 1996, Applied optics.

[39]  Charles L. Bennett,et al.  Infrared hyperspectral imaging results from vapor plume experiments , 1995, Defense, Security, and Sensing.

[40]  L. Scharf,et al.  Matched subspace detectors , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[41]  E. J. Kelly Performance of an adaptive detection algorithm; rejection of unwanted signals , 1989 .

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

[43]  Eric Truslow,et al.  Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms , 2014, IEEE Signal Processing Magazine.

[44]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .