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Elsevier DOI
0011
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Du, Q.[Qian],
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Du, Q.[Qian],
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Elsevier DOI
0702
Hyperspectral imagery; Classification;
Constrained linear discriminant analysis;
Unsupervised constrained linear discriminant analysis; Real-time processing
BibRef
Chang, C.I.[Chein-I],
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IEEE Top Reference.
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See also Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery.
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Liu, W.M.[Wei-Min],
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ICIP04(V: 3339-3342).
IEEE DOI
0505
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Xie, W.Y.[Wei-Ying],
Liu, B.Z.[Bao-Zhu],
Li, Y.S.[Yun-Song],
Lei, J.[Jie],
Chang, C.I.[Chein-I],
He, G.[Gang],
Spectral Adversarial Feature Learning for Anomaly Detection in
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IEEE DOI
2004
Feature extraction, Anomaly detection, Hyperspectral imaging,
Decoding, Image reconstruction, Training, Adversarial learning,
iterative optimization
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Jiang, T.[Tao],
Li, Y.S.[Yun-Song],
Xie, W.Y.[Wei-Ying],
Du, Q.[Qian],
Discriminative Reconstruction Constrained Generative Adversarial
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GeoRS(58), No. 7, July 2020, pp. 4666-4679.
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2006
Image reconstruction, Hyperspectral imaging, Feature extraction,
Detectors, Generative adversarial networks, Anomaly detection,
spatial-spectral detector
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Jiang, K.[Kai],
Xie, W.Y.[Wei-Ying],
Li, Y.S.[Yun-Song],
Lei, J.[Jie],
He, G.[Gang],
Du, Q.[Qian],
Semisupervised Spectral Learning With Generative Adversarial Network
for Hyperspectral Anomaly Detection,
GeoRS(58), No. 7, July 2020, pp. 5224-5236.
IEEE DOI
2006
Anomaly detection, Hyperspectral imaging,
Training, Feature extraction, Generative adversarial networks,
semisupervised learning
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Chang, C.I.,
Ren, H.[Hsuan],
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Chiang, S.S.[Shao-Shan],
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GeoRS(39), No. 7, July 2001, pp. 1380-1391.
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Chang, C.I.[Chein-I],
Target signature-constrained mixed pixel classification for
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Du, Q.[Qian],
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Real-time constrained linear discriminant analysis to target detection
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PR(36), No. 1, January 2003, pp. 1-12.
Elsevier DOI
0210
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Mayer, R.,
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GeoRS(40), No. 4, April 2002, pp. 831-840.
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Mayer, R.,
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Kwon, H.S.[Hee-Sung],
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0501
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Kwon, H.S.[Hee-Sung],
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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
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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
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SPLetters(15), No. 1, 2008, pp. 317-320.
IEEE DOI
0804
BibRef
Earlier:
Kernel-Based Spectral Matched Signal Detectors for Hyperspectral Target
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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
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SPMag(31), No. 1, January 2014, pp. 34-44.
IEEE DOI
1403
Survey, Hyperspectral Targets. hyperspectral imaging
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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
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Acito, N.,
Corsini, G.,
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Adaptive detection algorithm for full pixel targets in hyperspectral
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VISP(152), No. 6, December 2005, pp. 731-740.
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0512
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Acito, N.,
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A New Algorithm for Robust Estimation of the Signal Subspace in
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GeoRS(47), No. 11, November 2009, pp. 3844-3856.
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0911
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Acito, N.,
Diani, M.,
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Hyperspectral Signal Subspace Identification in the Presence of Rare
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1003
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Acito, N.,
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1104
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Acito, N.,
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Signal-Dependent Noise Modeling and Model Parameter Estimation in
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GeoRS(49), No. 8, August 2011, pp. 2957-2971.
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1108
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Acito, N.,
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On the CFAR Property of the RX Algorithm in the Presence of
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1307
anomaly detection robustness; Covariance matrix
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Acito, N.,
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Hyperspectral Signal Subspace Identification in the Presence of Rare
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Unsupervised Atmospheric Compensation of Airborne Hyperspectral
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1804
Aerosols, Atmospheric measurements, Atmospheric modeling,
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1305
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Matteoli, S.,
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Corsini, G.,
Impact of Signal Contamination on the Adaptive Detection Performance
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GeoRS(52), No. 4, April 2014, pp. 1948-1968.
IEEE DOI
1403
adaptive signal processing
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Acito, N.,
Corsini, G.,
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A Stochastic Mixing Model Approach to Sub-Pixel Target Detection in
Hyper-Spectral Images,
ICIP05(I: 653-656).
IEEE DOI
0512
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Acito, N.,
Matteoli, S.,
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Complexity-aware algorithm architecture for real-time enhancement of
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RealTimeIP(8), No. 1, March 2013, pp. 53-68.
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Rossi, A.,
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A Support Vector Method for Anomaly Detection in Hyperspectral Imagery,
GeoRS(44), No. 8, August 2006, pp. 2282-2291.
IEEE DOI
0608
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Banerjee, A.[Amit],
Burlina, P.[Philippe],
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Fast Hyperspectral Anomaly Detection via SVDD,
ICIP07(IV: 101-104).
IEEE DOI
0709
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Duran, O.,
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A Time-Efficient Method for Anomaly Detection in Hyperspectral Images,
GeoRS(45), No. 12, December 2007, pp. 3894-3904.
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0711
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Subpixel temporal spectral imaging,
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Elsevier DOI
1410
Remote sensing
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Duran, O.,
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Robust Endmember Extraction in the Presence of Anomalies,
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Anomaly Detection in Hyperspectral Imagery by Fuzzy Integral Fusion of
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An anomaly target detection algorithm in hyperspectral imagery through
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Elsevier DOI
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Hyperspectral imagery; Anomaly detection;
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0808
Hyperspectral imagery; Projection pursuit; Simulated annealing;
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GeoRS(49), No. 5, May 2011, pp. 1578-1589.
IEEE DOI
1105
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Du, B.,
Zhang, L.,
A Discriminative Metric Learning Based Anomaly Detection Method,
GeoRS(52), No. 11, November 2014, pp. 6844-6857.
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1407
Covariance matrices
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A Randomized Subspace Learning Based Anomaly Detector for
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Semisupervised Discriminative Locally Enhanced Alignment for
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GeoRS(51), No. 9, 2013, pp. 4800-4815.
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1309
Educational institutions
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1412
geophysical image processing
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Chen, Y.[Yu],
Anomaly Detection for Hyperspectral Imagery Based on the Regularized
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Target detection
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Hyperspectral imagery
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1112
Compressed-sensing. Analyze preservation of anomalies with random projections
used in compressive sensing.
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Johnson, R.J.,
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Bauer, K.W.,
AutoGAD: An Improved ICA-Based Hyperspectral Anomaly
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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
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geophysical image processing
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Anomaly detection
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Yuan, Y.[Yuan],
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Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing
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GeoRS(53), No. 2, February 2015, pp. 620-630.
IEEE DOI
1411
estimation theory
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Xu, Y.,
Wu, Z.,
Li, J.,
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Anomaly Detection in Hyperspectral Images Based on Low-Rank and
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1604
Detectors
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Zhan, T.M.[Tian-Ming],
Sun, L.[Le],
Xu, Y.[Yang],
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Xu, Y.,
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Joint Reconstruction and Anomaly Detection From Compressive
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1805
Anomaly detection, Compressed sensing, Hyperspectral imaging,
Image coding, Image reconstruction, Tensile stress,
robust principal component analysis (RPCA)
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Niu, Y.B.[Yu-Bin],
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GeoRS(55), No. 3, March 2017, pp. 1604-1617.
IEEE DOI
1703
Detectors
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Cheng, T.K.[Tong-Kai],
Wang, B.[Bin],
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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)
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Zhang, X.,
Wen, G.,
Dai, W.,
A Tensor Decomposition-Based Anomaly Detection Algorithm for
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GeoRS(54), No. 10, October 2016, pp. 5801-5820.
IEEE DOI
1610
Gaussian noise
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Zhou, J.,
Kwan, C.,
Ayhan, B.,
Eismann, M.T.,
A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection
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GeoRS(54), No. 11, November 2016, pp. 6497-6504.
IEEE DOI
1610
Approximation algorithms
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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
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Ma, D.[Dandan],
Yuan, Y.[Yuan],
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Hyperspectral Anomaly Detection via Discriminative Feature Learning
with Multiple-Dictionary Sparse Representation,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link
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Ma, D.[Dandan],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
Hyperspectral Anomaly Detection Based on Separability-Aware Sample
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RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
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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],
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Pascal, F.,
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Adaptive Nonzero-Mean Gaussian Detection,
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1702
Gaussian distribution
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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
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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
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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.
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Zhang, L.[Lili],
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Tensor decomposition-based sparsity divergence index for
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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.
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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
Mestav, K.R.,
Tong, L.,
Universal Data Anomaly Detection via Inverse Generative Adversary
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SPLetters(27), 2020, pp. 511-515.
IEEE DOI
2005
Anomaly detection, Generators, Training, Testing, Training data,
Quantization (signal), Machine learning, coincidence test
BibRef
Turkoz, M.[Mehmet],
Kim, S.[Sangahn],
Son, Y.[Youngdoo],
Jeong, M.K.[Myong K.],
Elsayed, E.A.[Elsayed A.],
Generalized support vector data description for anomaly detection,
PR(100), 2020, pp. 107119.
Elsevier DOI
2005
Anomaly detection, Bayesian statistics, Multimode process,
Support vector data description
BibRef
Chang, S.,
Du, B.,
Zhang, L.,
A Subspace Selection-Based Discriminative Forest Method for
Hyperspectral Anomaly Detection,
GeoRS(58), No. 6, June 2020, pp. 4033-4046.
IEEE DOI
2005
Anomaly detection axis-parallel subspace,
dimensionality reduction, hyperspectral imagery, isolation-based forest
BibRef
Zhang, J.,
Qing, L.,
Miao, J.,
Temporal Convolutional Network with Complementary Inner Bag Loss for
Weakly Supervised Anomaly Detection,
ICIP19(4030-4034)
IEEE DOI
1910
Anomaly detection, weakly-supervised learning, multiple instance learning
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
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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
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Carrera, D.,
Boracchi, G.,
Foi, A.,
Wohlberg, B.,
Scale-invariant anomaly detection with multiscale group-sparse models,
ICIP16(3892-3896)
IEEE DOI
1610
Detectors
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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
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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 .