14.2.2.7 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly

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

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

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

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, 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

Vafadar, M., Ghassemian, H.,
Hyperspectral anomaly detection using outlier removal from collaborative representation,
IPRIA17(13-19)
IEEE DOI 1712
geophysical image processing, hyperspectral imaging, image representation, remote sensing, AUC values, CRBORAD method, residual image 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 .

Last update:Mar 2, 2019 at 12:07:42