14.2.2.5 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

Niu, Y.[Yubin], 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.[Yubin], 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

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., Lee, L.C., Wang, Y., Xue, B., Song, M., Yu, C., 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, object detection, sensitivity analysis, 3D receiver operating characteristic curves, BSS-based hyperspectral anomaly detection, averaged least-squares error, background suppression, band subset selection, hyperspectral imagery, iterative process, target detection capability, virtual dimensionality, Detectors, Hyperspectral imaging, Object detection, Training, averaged least-squares error (ALSE), band selection (BS), dimensionality reduction (DR), sequential BSS (SQ-BSS), See also Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery. 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

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

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

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:Sep 22, 2017 at 21:00:01