Hyperspectral anomaly detection based on uniformly partitioned pixel
暂无分享,去创建一个
[1] Brian S. Penn. Using self-organizing maps for anomaly detection in hyperspectral imagery , 2002, Proceedings, IEEE Aerospace Conference.
[2] S Matteoli,et al. A tutorial overview of anomaly detection in hyperspectral images , 2010, IEEE Aerospace and Electronic Systems Magazine.
[3] A. Schaum. Joint subspace detection of hyperspectral targets , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).
[4] E. M. Winter,et al. Anomaly detection from hyperspectral imagery , 2002, IEEE Signal Process. Mag..
[5] Maria Petrou,et al. Spectral Unmixing With Negative and Superunity Abundances for Subpixel Anomaly Detection , 2009, IEEE Geoscience and Remote Sensing Letters.
[6] John P. Kerekes,et al. Unresolved target detection blind test project overview , 2010, Defense + Commercial Sensing.
[7] Heesung Kwon,et al. Kernel Eigenspace Separation Transform for Subspace Anomaly Detection in Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.
[8] John Ingram,et al. Hyperspectral anomaly detection based on minimum generalized variance method , 2008, SPIE Defense + Commercial Sensing.
[9] Alan P. Schaum,et al. Application of stochastic mixing models to hyperspectral detection problems , 1997, Defense, Security, and Sensing.
[10] Charles A. Bouman,et al. Evaluating and improving local hyperspectral anomaly detectors , 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
[11] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[12] A. P. Schaum,et al. Hyperspectral anomaly detection beyond RX , 2007, SPIE Defense + Commercial Sensing.
[13] I. Reed,et al. A Detection Algorithm for Optical Targets in Clutter , 1987, IEEE Transactions on Aerospace and Electronic Systems.
[14] J. Chanussot,et al. Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.
[15] Heesung Kwon,et al. Adaptive anomaly detection using subspace separation for hyperspectral imagery , 2003 .
[16] Edisanter Lo. Variable factorization model based on numerical optimization for hyperspectral anomaly detection , 2012, Pattern Analysis and Applications.
[17] Mehrdad Soumekh,et al. Hyperspectral anomaly detection within the signal subspace , 2006, IEEE Geoscience and Remote Sensing Letters.
[18] Chein-I Chang,et al. Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis , 2005, IEEE Trans. Geosci. Remote. Sens..
[19] Heesung Kwon,et al. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[20] Edisanter Lo. Variable subspace model for hyperspectral anomaly detection , 2011, Pattern Analysis and Applications.
[21] Xiaoli Yu,et al. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..
[22] Edisanter Lo. Maximized subspace model for hyperspectral anomaly detection , 2011, Pattern Analysis and Applications.
[23] John Ingram,et al. Algorithm for detecting anomaly in hyperspectral imagery using factor analysis , 2011, Defense + Commercial Sensing.
[24] Nasser M. Nasrabadi,et al. A comparative study of linear and nonlinear anomaly detectors for hyperspectral imagery , 2007, SPIE Defense + Commercial Sensing.