14.2.2.7 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly

Du, Q.[Qian], Chang, C.I.[Chein-I],
A linear constrained distance-based discriminant analysis for hyperspectral image classification,
PR(34), No. 2, February 2001, pp. 361-373.
Elsevier DOI 0011
BibRef

Chang, C.I.[C. I], Du, Q., Sun, T.L., Althouse, M.L.G.,
A Joint Band Prioritization and Band-Decorrelation Approach to Band Selection for Hyperspectral Image Classification,
GeoRS(7), No. 6, November 1999, pp. 2631.
IEEE Top Reference. 9911
BibRef

Du, Q.[Qian], Nekovei, R.[Reza],
Implementation of real-time constrained linear discriminant analysis to remote sensing image classification,
PR(38), No. 4, April 2005, pp. 459-471.
Elsevier DOI 0501
BibRef

Du, Q.[Qian], Nekovei, R.[Reza],
Fast real-time onboard processing of hyperspectral imagery for detection and classification,
RealTimeIP(4), No. 3, August 2009, pp. xx-yy.
Springer DOI 0909
BibRef

Du, Q.[Qian],
Unsupervised real-time constrained linear discriminant analysis to hyperspectral image classification,
PR(40), No. 5, May 2007, pp. 1510-1519.
Elsevier DOI 0702
Hyperspectral imagery; Classification; Constrained linear discriminant analysis; Unsupervised constrained linear discriminant analysis; Real-time processing BibRef

Chang, C.I.[Chein-I], Chiang, S.S.[Shao-Shan],
Anomaly detection and classification for hyperspectral imagery,
GeoRS(40), No. 6, June 2002, pp. 1314-1325.
IEEE Top Reference. 0208
See also Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. See also Hyperspectral Imaging: Techniques for Spectral Detection and Classification. BibRef

Chang, C.I.[Chein-I], Liu, W.M.[Wei-Min], Chang, C.C.[Chein-Chi],
Discrimination and identification for subpixel targets in hyperspectral imagery,
ICIP04(V: 3339-3342).
IEEE DOI 0505
BibRef

Chang, C.I., Ren, H.[Hsuan], Chiang, S.S.[Shao-Shan],
Real-time processing algorithms for target detection and classification in hyperspectral imagery,
GeoRS(39), No. 4, April 2001, pp. 760-768.
IEEE Top Reference. 0105
BibRef

Chiang, S.S.[Shao-Shan], Chang, C.I., Ginsberg, I.W.,
Unsupervised target detection in hyperspectral images using projection pursuit,
GeoRS(39), No. 7, July 2001, pp. 1380-1391.
IEEE Top Reference. 0108
BibRef

Chang, C.I.[Chein-I],
Target signature-constrained mixed pixel classification for hyperspectral imagery,
GeoRS(40), No. 5, May 2002, pp. 1065-1081.
IEEE Top Reference. 0206
BibRef

Du, Q.[Qian], Ren, H.[Hsuan],
Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery,
PR(36), No. 1, January 2003, pp. 1-12.
Elsevier DOI 0210
BibRef

Mayer, R., Priest, R.,
Object detection using transformed signatures in multitemporal hyperspectral imagery,
GeoRS(40), No. 4, April 2002, pp. 831-840.
IEEE Top Reference. 0206
BibRef

Mayer, R., Bucholtz, F., Scribner, D.,
Object detection by using 'whitening/dewhitening' to transform target signatures in multitemporal hyperspectral and multispectral imagery,
GeoRS(41), No. 5, May 2003, pp. 1136-1142.
IEEE Abstract. 0307
BibRef

Kwon, H.S.[Hee-Sung], Nasrabadi, N.M.,
Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery,
GeoRS(43), No. 2, February 2005, pp. 388-397.
IEEE Abstract. 0501
BibRef

Kwon, H.S.[Hee-Sung], Nasrabadi, N.M.[Nasser M.],
Kernel Matched Subspace Detectors for Hyperspectral Target Detection,
PAMI(28), No. 2, February 2006, pp. 178-194.
IEEE DOI 0601
BibRef

Kwon, H.S.[Hee-Sung], Nasrabadi, N.M.[Nasser M.],
Kernel Orthogonal Subspace Projection for Hyperspectral Signal Classification,
GeoRS(43), No. 12, December 2005, pp. 2952-2962.
IEEE DOI 0512
BibRef
Earlier:
Hyperspectral Target Detection Using Kernel Orthogonal Subspace Projection,
ICIP05(II: 702-705).
IEEE DOI 0512
BibRef
And:
Kernel Matched Signal Detectors for Hyperspectral Target Detection,
OTCBVS05(III: 6-6).
IEEE DOI 0507
BibRef
Earlier:
Hyperspectral target detection using kernel matched subspace detector,
ICIP04(V: 3327-3330).
IEEE DOI 0505
BibRef
Earlier:
Hyperspectral anomaly detection using kernel rx-algorithm,
ICIP04(V: 3331-3334).
IEEE DOI 0505
BibRef
Earlier:
Hyperspectral Target Detection Using Kernel Spectral Matched Filter,
OTCBVS04(127).
WWW Link. 0502
BibRef

Nasrabadi, N.M.[Nasser M.],
Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery,
SPLetters(15), No. 1, 2008, pp. 317-320.
IEEE DOI 0804
BibRef
Earlier:
Kernel-Based Spectral Matched Signal Detectors for Hyperspectral Target Detection,
PReMI07(67-76).
Springer DOI 0712
BibRef
And:
Regularized Spectral Matched Filter for Target Detection in Hyperspectral Imagery,
ICIP07(IV: 105-108).
IEEE DOI 0709
BibRef

Nasrabadi, N.M.,
Hyperspectral Target Detection : An Overview of Current and Future Challenges,
SPMag(31), No. 1, January 2014, pp. 34-44.
IEEE DOI 1403
Survey, Hyperspectral Targets. hyperspectral imaging BibRef

Kwon, H.S.[Hee-Sung], Nasrabadi, N.M.[Nasser M.],
Kernel Spectral Matched Filter for Hyperspectral Imagery,
IJCV(71), No. 2, February 2007, pp. 127-141.
Springer DOI 0609
BibRef

Kwon, H.S.[Hee-Sung], Der, S.Z., Nasrabadi, N.M.,
Projection-based adaptive anomaly detection for hyperspectral imagery,
ICIP03(I: 1001-1004).
IEEE DOI 0312
BibRef

Acito, N., Corsini, G., Diani, M.,
Adaptive detection algorithm for full pixel targets in hyperspectral images,
VISP(152), No. 6, December 2005, pp. 731-740.
DOI Link 0512
BibRef

Acito, N., Diani, M., Corsini, G.,
A New Algorithm for Robust Estimation of the Signal Subspace in Hyperspectral Images in the Presence of Rare Signal Components,
GeoRS(47), No. 11, November 2009, pp. 3844-3856.
IEEE DOI 0911
BibRef

Acito, N., Diani, M., Corsini, G.,
Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components,
GeoRS(48), No. 4, April 2010, pp. 1940-1954.
IEEE DOI 1003
BibRef

Acito, N., Diani, M., Corsini, G.,
Subspace-Based Striping Noise Reduction in Hyperspectral Images,
GeoRS(49), No. 4, April 2011, pp. 1325-1342.
IEEE DOI 1104
BibRef

Acito, N., Diani, M., Corsini, G.,
Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images,
GeoRS(49), No. 8, August 2011, pp. 2957-2971.
IEEE DOI 1108
BibRef

Acito, N., Diani, M., Corsini, G.,
On the CFAR Property of the RX Algorithm in the Presence of Signal-Dependent Noise in Hyperspectral Images,
GeoRS(51), No. 6, 2013, pp. 3475-3491.
IEEE DOI 1307
anomaly detection robustness; Covariance matrix BibRef

Acito, N., Diani, M., Corsini, G.,
Hyperspectral Signal Subspace Identification in the Presence of Rare Vectors and Signal-Dependent Noise,
GeoRS(51), No. 1, January 2013, pp. 283-299.
IEEE DOI 1301
BibRef

Acito, N., Diani, M.,
Unsupervised Atmospheric Compensation of Airborne Hyperspectral Images in the VNIR Spectral Range,
GeoRS(56), No. 4, April 2018, pp. 2083-2106.
IEEE DOI 1804
Aerosols, Atmospheric measurements, Atmospheric modeling, Hyperspectral imaging, Scattering, Atmospheric compensation (AC), radiative transfer model (RTM) BibRef

Matteoli, S., Acito, N., Diani, M., Corsini, G.,
An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection,
GeoRS(49), No. 2, February 2011, pp. 790-800.
IEEE DOI 1102
BibRef

Matteoli, S., Veracini, T., Diani, M., Corsini, G.,
Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection,
GeoRS(51), No. 5, May 2013, pp. 2837-2852.
IEEE DOI 1305
BibRef

Matteoli, S., Diani, M., Corsini, G.,
Impact of Signal Contamination on the Adaptive Detection Performance of Local Hyperspectral Anomalies,
GeoRS(52), No. 4, April 2014, pp. 1948-1968.
IEEE DOI 1403
adaptive signal processing BibRef

Acito, N., Corsini, G., Diani, M., Greco, M.,
A Stochastic Mixing Model Approach to Sub-Pixel Target Detection in Hyper-Spectral Images,
ICIP05(I: 653-656).
IEEE DOI 0512
BibRef

Acito, N., Matteoli, S., Diani, M., Corsini, G.,
Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images,
RealTimeIP(8), No. 1, March 2013, pp. 53-68.
WWW Link. 1303
BibRef

Rossi, A., Acito, N., Diani, M., Corsini, G.,
RX architectures for real-time anomaly detection in hyperspectral images,
RealTimeIP(9), No. 3, September 2014, pp. 503-517.
WWW Link. 1408
BibRef

Banerjee, A.[Amit], Burlina, P.[Philippe], Diehl, C.,
A Support Vector Method for Anomaly Detection in Hyperspectral Imagery,
GeoRS(44), No. 8, August 2006, pp. 2282-2291.
IEEE DOI 0608
BibRef

Banerjee, A.[Amit], Burlina, P.[Philippe], Meth, R.[Reuven],
Fast Hyperspectral Anomaly Detection via SVDD,
ICIP07(IV: 101-104).
IEEE DOI 0709
BibRef

Duran, O., Petrou, M.,
A Time-Efficient Method for Anomaly Detection in Hyperspectral Images,
GeoRS(45), No. 12, December 2007, pp. 3894-3904.
IEEE DOI 0711
BibRef

Duran, O., Petrou, M.,
Subpixel temporal spectral imaging,
PRL(48), No. 1, 2014, pp. 15-23.
Elsevier DOI 1410
Remote sensing BibRef

Duran, O., Petrou, M.,
Robust Endmember Extraction in the Presence of Anomalies,
GeoRS(49), No. 6, June 2011, pp. 1986-1996.
IEEE DOI 1106
BibRef

Di, W.[Wei], Pan, Q.[Quan], He, L.[Lin], Cheng, Y.M.[Yong-Mei],
Anomaly Detection in Hyperspectral Imagery by Fuzzy Integral Fusion of Band-subsets,
PhEngRS(74), No. 2, February 2008, pp. 201-214.
WWW Link. 0803
An anomaly target detection algorithm in hyperspectral imagery through merging detection results of band-subsets by a fuzzy integral fusion method. BibRef

He, L.[Lin], Pan, Q.[Quan], Di, W.[Wei], Li, Y.Q.[Yuan-Qing],
Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation,
PRL(29), No. 9, 1 July 2008, pp. 1392-1403.
Elsevier DOI 0711
Hyperspectral imagery; Anomaly detection; Maximum entropy and nonparametric estimation detector BibRef

Malpica, J.A.[Jose A.], Rejas, J.G.[Juan G.], Alonso, M.C.[Maria C.],
A projection pursuit algorithm for anomaly detection in hyperspectral imagery,
PR(41), No. 11, November 2008, pp. 3313-3327.
Elsevier DOI 0808
Hyperspectral imagery; Projection pursuit; Simulated annealing; Simplex optimization BibRef

Du, B., Zhang, L.,
Random-Selection-Based Anomaly Detector for Hyperspectral Imagery,
GeoRS(49), No. 5, May 2011, pp. 1578-1589.
IEEE DOI 1105
BibRef

Du, B., Zhang, L.,
A Discriminative Metric Learning Based Anomaly Detection Method,
GeoRS(52), No. 11, November 2014, pp. 6844-6857.
IEEE DOI 1407
Covariance matrices BibRef

Sun, W.W.[Wei-Wei], Tian, L.[Long], Xu, Y.[Yan], Du, B.[Bo], Du, Q.[Qian],
A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Shi, Q., Zhang, L., Du, B.,
Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification,
GeoRS(51), No. 9, 2013, pp. 4800-4815.
IEEE DOI 1309
Educational institutions BibRef

Li, W.[Wei], Du, Q.[Qian],
Collaborative Representation for Hyperspectral Anomaly Detection,
GeoRS(53), No. 3, March 2015, pp. 1463-1474.
IEEE DOI 1412
geophysical image processing BibRef

Tan, K.[Kun], Hou, Z.F.[Zeng-Fu], Wu, F.[Fuyu], Du, Q.[Qian], Chen, Y.[Yu],
Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Li, W.[Wei], Du, Q.[Qian], Zhang, B.[Bing],
Combined sparse and collaborative representation for hyperspectral target detection,
PR(48), No. 12, 2015, pp. 3904-3916.
Elsevier DOI 1509
Target detection BibRef

Li, W.[Wei], Du, Q.[Qian],
A survey on representation-based classification and detection in hyperspectral remote sensing imagery,
PRL(83, Part 2), No. 1, 2016, pp. 115-123.
Elsevier DOI 1609
Hyperspectral imagery BibRef

Fowler, J.E., Du, Q.,
Anomaly Detection and Reconstruction From Random Projections,
IP(21), No. 1, January 2012, pp. 184-195.
IEEE DOI 1112
Compressed-sensing. Analyze preservation of anomalies with random projections used in compressive sensing. BibRef

Khazai, S.[Safa], Safari, A.[Abdolreza], Mojaradi, B.[Barat], Homayouni, S.[Saeid],
Performance Comparison of Contemporary Anomaly Detectors for Detecting Man-Made Objects in Hyperspectral Images,
PFG(2013), No. 1, 2013, pp. 19-30.
DOI Link 1303
BibRef

Johnson, R.J., Williams, J.P., Bauer, K.W.,
AutoGAD: An Improved ICA-Based Hyperspectral Anomaly Detection Algorithm,
GeoRS(51), No. 6, 2013, pp. 3492-3503.
IEEE DOI 1307
covariance matrices; image denoising; independent component analysis; anomaly feature selection BibRef

Ratto, C.R., Morton, K.D., Collins, L.M., Torrione, P.A.,
Bayesian Context-Dependent Learning for Anomaly Classification in Hyperspectral Imagery,
GeoRS(52), No. 4, April 2014, pp. 1969-1981.
IEEE DOI 1403
geophysical image processing BibRef

Quinn, J.A.[John A.], Sugiyama, M.[Masashi],
A least-squares approach to anomaly detection in static and sequential data,
PRL(40), No. 1, 2014, pp. 36-40.
Elsevier DOI 1403
Anomaly detection BibRef

Yuan, Y.[Yuan], Wang, Q.[Qi], Zhu, G.[Guokang],
Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter,
GeoRS(53), No. 2, February 2015, pp. 620-630.
IEEE DOI 1411
estimation theory BibRef

Xu, Y., Wu, Z., Li, J., Plaza, A., Wei, Z.,
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation,
GeoRS(54), No. 4, April 2016, pp. 1990-2000.
IEEE DOI 1604
Detectors BibRef

Zhan, T.M.[Tian-Ming], Sun, L.[Le], Xu, Y.[Yang], Yang, G.W.[Guo-Wei], Zhang, Y.[Yan], Wu, Z.[Zebin],
Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Xu, Y., Wu, Z.[Zebin], Chanussot, J., Wei, Z.,
Joint Reconstruction and Anomaly Detection From Compressive Hyperspectral Images Using Mahalanobis Distance-Regularized Tensor RPCA,
GeoRS(56), No. 5, May 2018, pp. 2919-2930.
IEEE DOI 1805
Anomaly detection, Compressed sensing, Hyperspectral imaging, Image coding, Image reconstruction, Tensile stress, robust principal component analysis (RPCA) BibRef

Niu, Y.B.[Yu-Bin], Wang, B.[Bin],
Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary,
RS(8), No. 4, 2016, pp. 289.
DOI Link 1604
BibRef

Niu, Y.B.[Yu-Bin], Wang, B.[Bin],
Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary,
GeoRS(55), No. 3, March 2017, pp. 1604-1617.
IEEE DOI 1703
Detectors BibRef

Zhang, X., Wen, G., Dai, W.,
A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image,
GeoRS(54), No. 10, October 2016, pp. 5801-5820.
IEEE DOI 1610
Gaussian noise BibRef

Zhou, J., Kwan, C., Ayhan, B., Eismann, M.T.,
A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images,
GeoRS(54), No. 11, November 2016, pp. 6497-6504.
IEEE DOI 1610
Approximation algorithms BibRef

Yuan, Y., Ma, D., Wang, Q.,
Hyperspectral Anomaly Detection by Graph Pixel Selection,
Cyber(46), No. 12, December 2016, pp. 3123-3134.
IEEE DOI 1612
Detectors BibRef

Delibalta, I.[Ibrahim], Gokcesu, K.[Kaan], Simsek, M.[Mustafa], Baruh, L.[Lemi], Kozat, S.S.[Suleyman S.],
Online Anomaly Detection With Nested Trees,
SPLetters(23), No. 12, December 2016, pp. 1867-1871.
IEEE DOI 1612
decision trees BibRef

Zhao, R., Du, B., Zhang, L., Zhang, L.,
Beyond Background Feature Extraction: An Anomaly Detection Algorithm Inspired by Slowly Varying Signal Analysis,
GeoRS(54), No. 3, March 2016, pp. 1757-1774.
IEEE DOI 1603
Detectors BibRef

Zhao, R., Du, B., Zhang, L.,
Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework,
GeoRS(55), No. 6, June 2017, pp. 3208-3222.
IEEE DOI 1706
Detectors, Dictionaries, Encoding, Estimation, Hyperspectral imaging, Sparse matrices, Anomaly detection, K-SVD algorithm, dictionary enhancement, hyperspectral, negative log atom usage probability, sparse coding, sparsity, score, estimation BibRef

Zhao, C.H.[Chun-Hui], Yao, X.F.[Xi-Feng], Huang, B.[Bormin],
Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm,
RS(8), No. 12, 2016, pp. 1011.
DOI Link 1612
BibRef

Frontera-Pons, J., Pascal, F., Ovarlez, J.P.,
Adaptive Nonzero-Mean Gaussian Detection,
GeoRS(55), No. 2, February 2017, pp. 1117-1124.
IEEE DOI 1702
Gaussian distribution BibRef

Frontera-Pons, J., Ovarlez, J.P., Pascal, F.,
Robust ANMF Detection in Noncentered Impulsive Background,
SPLetters(24), No. 12, December 2017, pp. 1891-1895.
IEEE DOI 1712
Covariance matrices, Detectors, Maximum likelihood estimation, Object detection, Parameter estimation, Robustness, M-estimation, robustness BibRef

Veganzones, M.Á.[Miguel Ángel], Frontera-Pons, J., Pascal, F., Ovarlez, J.P., Chanussot, J.[Jocelyn],
Binary Partition Trees-Based Robust Adaptive Hyperspectral RX Anomaly Detection,
ICIP14(5077-5081)
IEEE DOI 1502
Detectors See also Context-Adaptive Pansharpening Based on Binary Partition Tree Segmentation. BibRef

Veganzones, M.Á.[Miguel Ángel], Mura, M.D.[Mauro Dalla], Tochon, G.[Guillaume], Chanussot, J.[Jocelyn],
Binary Partition Trees-Based Spectral-Spatial Permutation Ordering,
ISMM15(434-445).
Springer DOI 1506
BibRef

Wang, L., Chang, C.I.[Chein-I], Lee, L.C., Wang, Y., Xue, B., Song, M.P.[Mei-Ping], Yu, C.Y.[Chun-Yan], Li, S.,
Band Subset Selection for Anomaly Detection in Hyperspectral Imagery,
GeoRS(55), No. 9, September 2017, pp. 4887-4898.
IEEE DOI 1709
geophysical image processing, hyperspectral imaging, iterative methods, least squares approximations, See also Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery. BibRef

Yu, C.Y.[Chun-Yan], Song, M.P.[Mei-Ping], Chang, C.I.[Chein-I],
Band Subset Selection for Hyperspectral Image Classification,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Zhang, L.[Lili], Zhao, C.H.[Chun-Hui],
Tensor decomposition-based sparsity divergence index for hyperspectral anomaly detection,
JOSA-A(34), No. 9, September 2017, pp. 1585-1594.
DOI Link 1709
Digital image processing, Image analysis BibRef

Kang, X., Zhang, X., Li, S., Li, K., Li, J., Benediktsson, J.A.,
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters,
GeoRS(55), No. 10, October 2017, pp. 5600-5611.
IEEE DOI 1710
geophysical techniques, Boolean map-based fusion approach, edge-preserving filters, hyperspectral anomaly detection, BibRef

Zhao, L.Y.[Liao-Ying], Lin, W.J.[Wei-Jun], Wang, Y.[Yulei], Li, X.R.[Xiao-Run],
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Kittler, J.[Josef], Zor, C.[Cemre], Kaloskampis, I.[Ioannis], Hicks, Y.[Yulia], Wang, W.[Wenwu],
Error sensitivity analysis of Delta divergence-a novel measure for classifier incongruence detection,
PR(77), 2018, pp. 30-44.
Elsevier DOI 1802
Anomaly detection, Classifier decision incongruence, Bayesian surprise BibRef

Soofbaf, S.R.[Seyyed Reza], Sahebi, M.R.[Mahmod Reza], Mojaradi, B.[Barat],
A Sliding Window-Based Joint Sparse Representation (SWJSR) Method for Hyperspectral Anomaly Detection,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Zhu, L.X.[Ling-Xiao], Wen, G.J.[Gong-Jian],
Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Imani, M.[Maryam],
Hyperspectral anomaly detection using differential image,
IET-IPR(12), No. 5, May 2018, pp. 801-809.
DOI Link 1804
BibRef

Mohammadi-Ghazi, R.[Reza], Marzouk, Y.M.[Youssef M.], Büyüköztürk, O.[Oral],
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection,
PR(81), 2018, pp. 601-614.
Elsevier DOI 1806
Novelty detection, Mixture models, Graphical models, Conditional dependence, Conditional density, False positive BibRef

Zhu, L.[Lingxiao], Wen, G.[Gongjian], Qiu, S.H.[Shao-Hua],
Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Ma, D.[Dandan], Yuan, Y.[Yuan], Wang, Q.[Qi],
Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chang, S., Du, B., Zhang, L.,
BASO: A Background-Anomaly Component Projection and Separation Optimized Filter for Anomaly Detection in Hyperspectral Images,
GeoRS(56), No. 7, July 2018, pp. 3747-3761.
IEEE DOI 1807
geophysical image processing, hyperspectral imaging, image segmentation, matched filters, object detection, matched filter BibRef

Li, F., Zhang, X., Zhang, L., Jiang, D., Zhang, Y.,
Exploiting Structured Sparsity for Hyperspectral Anomaly Detection,
GeoRS(56), No. 7, July 2018, pp. 4050-4064.
IEEE DOI 1807
Bayes methods, geophysical image processing, hyperspectral imaging, image reconstruction, structured sparse representation BibRef

Qu, Y., Wang, W.[Wei], Guo, R., Ayhan, B.[Bulent], Kwan, C.[Chiman], Vance, S.[Steven], Qi, H.R.[Hai-Rong],
Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition,
GeoRS(56), No. 8, August 2018, pp. 4391-4405.
IEEE DOI 1808
hyperspectral imaging, matrix decomposition, object detection, pattern clustering, vectors, abundance vectors, spectral unmixing BibRef

Li, S.J.[Shuang-Jiang], Wang, W.[Wei], Qi, H.R.[Hai-Rong], Ayhan, B.[Bulent], Kwan, C.[Chiman], Vance, S.[Steven],
Low-rank tensor decomposition based anomaly detection for hyperspectral imagery,
ICIP15(4525-4529)
IEEE DOI 1512
Hyperspectral imaging BibRef

Yang, Y.X.[Yi-Xin], Zhang, J.Q.[Jian-Qi], Song, S.Z.[Shang-Zhen], Liu, D.L.[De-Lian],
Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Xie, W.B.[Wen-Bin], Yin, H.[Hong], Wang, M.N.[Mei-Ni], Shao, Y.[Yan], Yu, B.[Bosi],
Low-rank structured sparse representation and reduced dictionary learning-based abnormity detection,
IET-CV(13), No. 1, February 2019, pp. 8-14.
DOI Link 1902
BibRef

Ling, Q., Guo, Y., Lin, Z., An, W.,
A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection,
GeoRS(57), No. 4, April 2019, pp. 2358-2371.
IEEE DOI 1904
computational complexity, feature extraction, image representation, mixture models, object detection, linear mixture model (LMM) BibRef

Huyan, N., Zhang, X., Zhou, H., Jiao, L.,
Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction,
GeoRS(57), No. 4, April 2019, pp. 2263-2276.
IEEE DOI 1904
dictionaries, hyperspectral imaging, image representation, matrix decomposition, object detection, remote sensing, potential anomaly dictionary BibRef

Zhang, J., Wang, Z., Meng, J., Tan, Y., Yuan, J.,
Boosting Positive and Unlabeled Learning for Anomaly Detection With Multi-Features,
MultMed(21), No. 5, May 2019, pp. 1332-1344.
IEEE DOI 1905
learning (artificial intelligence), pattern classification, machine learning-based anomaly detection, anomaly data, boosting BibRef

Ma, N.[Ning], Yu, X.[Ximing], Peng, Y.[Yu], Wang, S.[Shaojun],
A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Madathil, B., George, S.N.,
Simultaneous Reconstruction and Anomaly Detection of Subsampled Hyperspectral Images Using l_(1/2) Regularized Joint Sparse and Low-Rank Recovery,
GeoRS(57), No. 7, July 2019, pp. 5190-5197.
IEEE DOI 1907
Anomaly detection, Image reconstruction, Hyperspectral imaging, Data models, Sparse matrices, Detectors, Anomaly detection, l(1/2) regularization BibRef

Tan, K.[Kun], Hou, Z.F.[Zeng-Fu], Ma, D.[Donglei], Chen, Y.[Yu], Du, Q.[Qian],
Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Kulczycki, P.[Piotr], Kruszewski, D.[Damian],
Detection of Atypical Elements by Transforming Task to Supervised Form,
PReMI17(458-466).
Springer DOI 1711
Atypical elemnent in data set. BibRef

Olson, C.C., Doster, T.,
A Novel Detection Paradigm and Its Comparison to Statistical and Kernel-Based Anomaly Detection Algorithms for Hyperspectral Imagery,
PBVS17(302-308)
IEEE DOI 1709
Anomaly detection, Data models, Detectors, Hyperspectral imaging, Kernel, Principal component analysis, Skeleton BibRef

Ayuga, J.G.R.[J. G. Rejas], Marín, R.M.[R. Martínez], Sacristán, M.M.[M. Marchamalo], Bonatti, J., Ojeda, J.C.,
Hyperspectral Anomaly Detection In Urban Scenarios,
ISPRS16(B7: 111-116).
DOI Link 1610
BibRef

Xiao, T.[Tan], Zhang, C.[Chao], Zha, H.B.[Hong-Bin], Wei, F.Y.[Fang-Yun],
Anomaly Detection via Local Coordinate Factorization and Spatio-Temporal Pyramid,
ACCV14(V: 66-82).
Springer DOI 1504
BibRef

Hachiya, H., Matsugu, M.,
NSH: Normality Sensitive Hashing for Anomaly Detection,
VECTaR13(795-802)
IEEE DOI 1403
Locality sensitive hashing. cryptography BibRef

Chen, G.L.[Guang-Liang], Iwen, M.[Mark], Chin, S.[Sang], Maggioni, M.[Mauro],
A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Rejas, J.G., Martínez-Frías, J., Bonatti, J., Martínez, R., Marchamalo, M.,
Anomaly Detection And Comparative Analysis Of Hydrothermal Alteration Materials Trough Hyperspectral Multisensor Data In The Turrialba Volcano,
ISPRS12(XXXIX-B7:151-155).
DOI Link 1209
BibRef

Du, B., Zhang, L., Xin, H.,
Robust Metric based Anomaly Detection in Kernel Feature Space,
ISPRS12(XXXIX-B7:113-119).
DOI Link 1209
BibRef

Carrera, D., Boracchi, G., Foi, A., Wohlberg, B.,
Scale-invariant anomaly detection with multiscale group-sparse models,
ICIP16(3892-3896)
IEEE DOI 1610
Detectors BibRef

Theiler, J.[James], Wohlberg, B.[Brendt],
Detection of spectrally sparse anomalies in hyperspectral imagery,
Southwest12(117-120).
IEEE DOI 1205
BibRef

Bachega, L.R.[Leonardo R.], Theiler, J.[James], Bouman, C.A.[Charles A.],
Evaluating and improving local hyperspectral anomaly detectors,
AIPR11(1-8).
IEEE DOI 1204
BibRef

Nasrabadi, N.M.[Nasser M.],
A nonlinear kernel-based joint fusion/detection of anomalies using Hyperspectral and SAR imagery,
ICIP08(1864-1867).
IEEE DOI 0810
BibRef

Huck, A.[Alexis], Guillaume, M.[Mireille],
A CFAR algorithm for anomaly detection and discrimination in hyperspectral images,
ICIP08(1868-1871).
IEEE DOI 0810
BibRef

Huck, A.[Alexis], Guillaume, M.[Mireille],
Independent Component Analysis-Based Estimation of Anomaly Abundances in Hyperspectral Images,
ACIVS07(168-177).
Springer DOI 0708
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unsupervised Clustering, Classification .