14.2.2.5 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly

Chapter Contents (Back)
Hyperspectral. Anomaly Detection.

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
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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
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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.
WWW Link. 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

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.
WWW Link. 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.
WWW Link. 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

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

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

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


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:Nov 18, 2017 at 20:56:18