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
Cheng, T.K.[Tong-Kai],
Wang, B.[Bin],
Graph and Total Variation Regularized Low-Rank Representation for
Hyperspectral Anomaly Detection,
GeoRS(58), No. 1, January 2020, pp. 391-406.
IEEE DOI
2001
Hyperspectral imaging, Anomaly detection, Detectors,
Object detection, Manifolds, TV, Anomaly detection,
total variation (TV)
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.[Yuan],
Ma, D.[Dandan],
Wang, Q.[Qi],
Hyperspectral Anomaly Detection by Graph Pixel Selection,
Cyber(46), No. 12, December 2016, pp. 3123-3134.
IEEE DOI
1612
Detectors
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
Ma, D.[Dandan],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
Hyperspectral Anomaly Detection Based on Separability-Aware Sample
Cascade,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
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.X.[Ling-Xiao],
Wen, G.J.[Gong-Jian],
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
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
Zhang, W.X.[Wu-Xia],
Lu, X.Q.[Xiao-Qiang],
Li, X.L.[Xue-Long],
Similarity Constrained Convex Nonnegative Matrix Factorization for
Hyperspectral Anomaly Detection,
GeoRS(57), No. 7, July 2019, pp. 4810-4822.
IEEE DOI
1907
Anomaly detection, Hyperspectral imaging, Detectors,
Sparse matrices, Matrix decomposition, Dictionaries,
similarity constrained
BibRef
Huang, Z.,
Li, S.,
From Difference to Similarity:
A Manifold Ranking-Based Hyperspectral Anomaly Detection Framework,
GeoRS(57), No. 10, October 2019, pp. 8118-8130.
IEEE DOI
1910
feature extraction, graph theory, hyperspectral imaging,
image segmentation, object detection, anomaly pixels, similarity
BibRef
Díaz, M.,
Guerra, R.,
Horstrand, P.,
López, S.,
Sarmiento, R.,
A Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery,
GeoRS(57), No. 11, November 2019, pp. 8968-8982.
IEEE DOI
1911
Hyperspectral imaging, Detectors, Real-time systems,
Covariance matrices, Computational complexity,
real-time applications
BibRef
Tu, B.[Bing],
Li, N.Y.[Nan-Ying],
Liao, Z.L.[Zhuo-Lang],
Ou, X.F.[Xian-Feng],
Zhang, G.Y.[Guo-Yun],
Hyperspectral Anomaly Detection via Spatial Density Background
Purification,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Tu, B.[Bing],
Yang, X.C.[Xian-Chang],
Li, N.Y.[Nan-Ying],
Zhou, C.[Chengle],
He, D.B.[Dan-Bing],
Hyperspectral anomaly detection via density peak clustering,
PRL(129), 2020, pp. 144-149.
Elsevier DOI
2001
Anomaly detection, Density peak clustering, Hyperspectral image
BibRef
Zhao, C.H.[Chun-Hui],
Yao, X.F.[Xi-Feng],
Progressive line processing of global and local real-time anomaly
detection in hyperspectral images,
RealTimeIP(16), No. 6, December 2019, pp. 2289-2303.
WWW Link.
1912
BibRef
Marchetti, Y.[Yuliya],
Rosenberg, R.[Robert],
Crisp, D.[David],
Classification of Anomalous Pixels in the Focal Plane Arrays of
Orbiting Carbon Observatory-2 and -3 via Machine Learning,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Xiang, P.[Pei],
Song, J.[Jiangluqi],
Li, H.[Huan],
Gu, L.[Lin],
Zhou, H.[Huixin],
Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank
Decomposition,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Li, S.,
Zhang, K.,
Duan, P.,
Kang, X.,
Hyperspectral Anomaly Detection With Kernel Isolation Forest,
GeoRS(58), No. 1, January 2020, pp. 319-329.
IEEE DOI
2001
Kernel, Anomaly detection, Hyperspectral imaging, Detectors,
Vegetation, Forestry, Anomaly detection, hyperspectral image (HSI),
kernel method
BibRef
Lu, X.Q.[Xiao-Qiang],
Zhang, W.X.[Wu-Xia],
Huang, J.[Ju],
Exploiting Embedding Manifold of Autoencoders for Hyperspectral
Anomaly Detection,
GeoRS(58), No. 3, March 2020, pp. 1527-1537.
IEEE DOI
2003
Hyperspectral imaging, Anomaly detection, Manifolds,
Learning systems, Image reconstruction, Task analysis,
manifold learning
BibRef
Huang, Z.,
Kang, X.,
Li, S.,
Hao, Q.,
Game Theory-Based Hyperspectral Anomaly Detection,
GeoRS(58), No. 4, April 2020, pp. 2965-2976.
IEEE DOI
2004
Anomaly detection, decision fusion, game theory,
hyperspectral images (HSIs), Nash equilibrium, spectral-spatial information
BibRef
Al-Sarayreh, M.[Mahmoud],
Reis, M.M.[Marlon M.],
Yan, W.Q.[Wei Qi],
Klette, R.[Reinhard],
A Sequential CNN Approach for Foreign Object Detection in Hyperspectral
Images,
CAIP19(I:271-283).
Springer DOI
1909
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 .