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0702
Hyperspectral imagery; Classification;
Constrained linear discriminant analysis;
Unsupervised constrained linear discriminant analysis; Real-time processing
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Chang, C.I.[Chein-I],
He, G.[Gang],
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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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Du, Q.[Qian],
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GeoRS(59), No. 7, July 2021, pp. 6017-6028.
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2106
Anomaly detection, Hyperspectral imaging,
Generative adversarial networks, Training,
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See also Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning.
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Jiang, K.[Kai],
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Semisupervised Spectral Learning With Generative Adversarial Network
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IEEE DOI
2006
Anomaly detection, Hyperspectral imaging,
Training, Feature extraction, Generative adversarial networks,
semisupervised learning
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GeoRS(58), No. 8, August 2020, pp. 5416-5427.
IEEE DOI
2007
Anomaly detection, Hyperspectral imaging, Estimation, Training,
Feature extraction, Data models, Anomaly detection,
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See also Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder.
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Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture
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GeoRS(59), No. 11, November 2021, pp. 9504-9517.
IEEE DOI
2111
Anomaly detection, Hyperspectral imaging, Gaussian distribution,
Correlation, Partitioning algorithms, Gaussian mixture model,
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1412
geophysical image processing
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Target detection
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Compressed-sensing. Analyze preservation of anomalies with random projections
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Du, B.,
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1407
Covariance matrices
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1309
Educational institutions
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2006
Image reconstruction, Hyperspectral imaging, Feature extraction,
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2009
Anomaly detection, Hyperspectral imaging, Image reconstruction,
Decoding, Detectors, Detection algorithms, Anomaly detection,
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See also Characterization of Background-Anomaly Separability With Generative Adversarial Network for Hyperspectral Anomaly Detection.
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Sparse matrices, Object detection, Matrix decomposition,
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0512
BibRef
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Hyperspectral Target Detection Using Kernel Orthogonal Subspace
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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
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Hyperspectral Target Detection Using Kernel Spectral Matched Filter,
OTCBVS04(127).
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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
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ICIP07(IV: 105-108).
IEEE DOI
0709
BibRef
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Survey, Hyperspectral Targets. hyperspectral imaging
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anomaly detection robustness; Covariance matrix
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Aerosols, Atmospheric measurements, Atmospheric modeling,
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adaptive signal processing
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Remote sensing
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Hyperspectral imagery; Anomaly detection;
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covariance matrices; image denoising; independent component analysis;
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geophysical image processing
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estimation theory
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Anomaly detection, Compressed sensing, Hyperspectral imaging,
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Gaussian noise
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decision trees
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Detectors, Dictionaries, Encoding, Estimation, Hyperspectral imaging,
Sparse matrices, Anomaly detection, K-SVD algorithm,
dictionary enhancement, hyperspectral,
negative log atom usage probability, sparse coding, sparsity,
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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
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.[Lin],
Chang, C.I.[Chein-I],
Lee, L.C.[Li-Chien],
Wang, Y.[Yulei],
Xue, B.[Bai],
Song, M.P.[Mei-Ping],
Yu, C.Y.[Chuan-Yan],
Li, S.[Sen],
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
Lei, J.[Jie],
Xie, W.Y.[Wei-Ying],
Yang, J.[Jian],
Li, Y.S.[Yun-Song],
Chang, C.I.[Chein-I],
Spectral-Spatial Feature Extraction for Hyperspectral Anomaly
Detection,
GeoRS(57), No. 10, October 2019, pp. 8131-8143.
IEEE DOI
1910
feature extraction, hyperspectral imaging, image filtering,
image representation, learning (artificial intelligence),
interference suppression
BibRef
Xie, W.Y.[Wei-Ying],
Li, Y.S.[Yun-Song],
Lei, J.[Jie],
Yang, J.[Jian],
Chang, C.I.[Chein-I],
Li, Z.[Zhen],
Hyperspectral Band Selection for Spectral-Spatial Anomaly Detection,
GeoRS(58), No. 5, May 2020, pp. 3426-3436.
IEEE DOI
2005
Anomaly detection, band selection, hyperspectral image (HSI),
spectral-spatial optimization, unsupervised representation learning
See also Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection.
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
Chang, C.I.[Chein-I],
Kuo, Y.M.[Yi-Mei],
Chen, S.H.[Shu-Han],
Liang, C.C.[Chia-Chen],
Ma, K.Y.[Kenneth Yeonkong],
Hu, P.F.M.[Peter Fu-Ming],
Self-Mutual Information-Based Band Selection for Hyperspectral Image
Classification,
GeoRS(59), No. 7, July 2021, pp. 5979-5997.
IEEE DOI
2106
Hyperspectral imaging, Entropy, Correlation,
Extraterrestrial measurements, Probability distribution, Sensors,
virtual dimensionality (VD)
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
Zhang, L.[Lili],
Ma, J.C.[Jia-Chen],
Cheng, B.Z.[Bao-Zhi],
Lin, F.[Fang],
Fractional Fourier Transform-Based Tensor RX for Hyperspectral
Anomaly Detection,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Feng, S.[Shou],
Tang, S.[Shulu],
Zhao, C.H.[Chun-Hui],
Cui, Y.[Ying],
A Hyperspectral Anomaly Detection Method Based on Low-Rank and Sparse
Decomposition With Density Peak Guided Collaborative Representation,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI
2112
Sparse matrices, Hyperspectral imaging, Detectors,
Matrix decomposition, Covariance matrices, Collaboration,
low-rank and sparse decomposition
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.V.[Josef V.],
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
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
Song, S.Z.[Shang-Zhen],
Yang, Y.X.[Yi-Xin],
Zhou, H.X.[Hui-Xin],
Chan, J.C.W.[Jonathan Cheung-Wai],
Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank
Decomposition with Texture Feature Extraction,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Xiang, P.[Pei],
Song, J.[Jiangluqi],
Li, H.[Huan],
Gu, L.[Lin],
Zhou, H.X.[Hui-Xin],
Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank
Decomposition,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
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
Ning, H.Y.[Hu-Yan],
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.M.[Xi-Ming],
Peng, Y.[Yu],
Wang, S.J.[Shao-Jun],
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
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
Network,
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
Huang, Z.,
Fang, L.,
Li, S.,
Subpixel-Pixel-Superpixel Guided Fusion for Hyperspectral Anomaly
Detection,
GeoRS(58), No. 9, September 2020, pp. 5998-6007.
IEEE DOI
2008
Feature extraction, Detectors, Hyperspectral imaging,
Anomaly detection, Optimization, Object detection,
subpixel
BibRef
Wang, R.,
Nie, F.,
Wang, Z.,
He, F.,
Li, X.,
Multiple Features and Isolation Forest-Based Fast Anomaly Detector
for Hyperspectral Imagery,
GeoRS(58), No. 9, September 2020, pp. 6664-6676.
IEEE DOI
2008
Feature extraction, Hyperspectral imaging, Anomaly detection,
Clustering algorithms, Forestry, Detectors,
multiple features
BibRef
Li, Z.X.[Zhao-Xu],
Ling, Q.A.[Qi-Ang],
Wu, J.[Jing],
Wang, Z.Y.[Zheng-Yan],
Lin, Z.P.[Zai-Ping],
A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly
Detector for Moving Targets in Hyperspectral Imagery Sequences,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Wang, S.Y.[Shao-Yu],
Wang, X.Y.[Xin-Yu],
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Hyperspectral Anomaly Detection via Locally Enhanced Low-Rank Prior,
GeoRS(58), No. 10, October 2020, pp. 6995-7009.
IEEE DOI
2009
Anomaly detection, Hyperspectral imaging, Dictionaries,
Sparse matrices, Image segmentation, Matrix decomposition,
matrix decomposition
BibRef
Zhu, X.[Xuhe],
Cao, L.Q.[Li-Qin],
Wang, S.Y.[Shao-Yu],
Gao, L.Z.[Lyu-Zhou],
Zhong, Y.F.[Yan-Fei],
Anomaly Detection in Airborne Fourier Transform Thermal Infrared
Spectrometer Images Based on Emissivity and a Segmented Low-Rank
Prior,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Andika, F.[Ferdi],
Rizkinia, M.[Mia],
Okuda, M.[Masahiro],
A Hyperspectral Anomaly Detection Algorithm Based on Morphological
Profile and Attribute Filter with Band Selection and Automatic
Determination of Maximum Area,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Su, H.J.[Hong-Jun],
Wu, Z.Y.[Zhao-Yue],
Zhu, A.X.[A-Xing],
Du, Q.[Qian],
Low rank and collaborative representation for hyperspectral anomaly
detection via robust dictionary construction,
PandRS(169), 2020, pp. 195-211.
Elsevier DOI
2011
Low-rank representation, Collaborative representation,
Dictionary construction, Anomaly detection, Hyperspectral image
BibRef
Tu, B.,
Yang, X.,
Zhou, C.,
He, D.,
Plaza, A.,
Hyperspectral Anomaly Detection Using Dual Window Density,
GeoRS(58), No. 12, December 2020, pp. 8503-8517.
IEEE DOI
2012
Anomaly detection, Detectors, Hyperspectral imaging,
Contamination, Covariance matrices,
intrinsic image decomposition (IID)
BibRef
Cheng, T.,
Wang, B.,
Total Variation and Sparsity Regularized Decomposition Model With
Union Dictionary for Hyperspectral Anomaly Detection,
GeoRS(59), No. 2, February 2021, pp. 1472-1486.
IEEE DOI
2101
Hyperspectral imaging, Dictionaries, Anomaly detection, Detectors,
Object detection, TV, Anomaly detection,
total variation (TV)
BibRef
Li, Z.A.[Zhu-Ang],
Zhang, Y.[Ye],
Hyperspectral Anomaly Detection via Image Super-Resolution Processing
and Spatial Correlation,
GeoRS(59), No. 3, March 2021, pp. 2307-2320.
IEEE DOI
2103
Anomaly detection, Correlation, Spatial resolution,
Hyperspectral imaging, Object detection, Anomaly detection,
super-resolution (SR)
BibRef
Chang, C.I.,
Cao, H.,
Chen, S.,
Shang, X.,
Yu, C.,
Song, M.,
Orthogonal Subspace Projection-Based Go-Decomposition Approach to
Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly
Detection,
GeoRS(59), No. 3, March 2021, pp. 2403-2429.
IEEE DOI
2103
Sparse matrices, Matrix decomposition, Hyperspectral imaging,
Anomaly detection, Iterative algorithms,
virtual dimensionality (VD)
BibRef
Li, Z.H.[Zhong-Heng],
He, F.[Fang],
Hu, H.J.[Hao-Jie],
Wang, F.[Fei],
Yu, W.Z.[Wei-Zhong],
Random Collective Representation-Based Detector with Multiple
Features for Hyperspectral Images,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Shibi, S.[Sherin],
Rajagopal, G.[Gayathri],
Target object detection using chicken social-based deep belief network
with hyperspectral imagery,
IET-IPR(14), No. 15, 15 December 2020, pp. 3781-3790.
DOI Link
2103
Objects as anomalies.
Integrating the chicken swarm optimisation with
the social ski-driver algorithm.
BibRef
Mishra, P.[Pankaj],
Piciarelli, C.[Claudio],
Foresti, G.L.[Gian Luca],
Image Anomaly Detection by Aggregating Deep Pyramidal Representations,
IML20(705-718).
Springer DOI
2103
BibRef
Sun, X.T.[Xiao-Tong],
Qu, Y.[Ying],
Gao, L.[Lianru],
Sun, X.[Xu],
Qi, H.R.[Hai-Rong],
Zhang, B.[Bing],
Shen, T.[Ting],
Target Detection Through Tree-Structured Encoding for Hyperspectral
Images,
GeoRS(59), No. 5, May 2021, pp. 4233-4249.
IEEE DOI
2104
Object detection, Hyperspectral imaging, Detectors, Vegetation,
Encoding, Binary trees, Binary trees, encoding,
target detection
BibRef
Taghipour, A.[Ashkan],
Ghassemian, H.[Hassan],
A bottom-up and top-down human visual attention approach for
hyperspectral anomaly detection,
JVCIR(77), 2021, pp. 103113.
Elsevier DOI
2106
Hyperspectral image, Visual attention, Anomaly detection,
Bottom-up attention, Top-down attention
BibRef
Kurt, M.N.[Mehmet Necip],
Yilmaz, Y.[Yasin],
Wang, X.D.[Xiao-Dong],
Real-Time Nonparametric Anomaly Detection in High-Dimensional
Settings,
PAMI(43), No. 7, July 2021, pp. 2463-2479.
IEEE DOI
2106
Anomaly detection, Real-time systems, Data models,
Approximation algorithms, Reliability,
cumulative sum (CUSUM)
BibRef
Zhang, Z.[Zheng],
Deng, X.G.[Xiao-Gang],
Anomaly detection using improved deep SVDD model with data structure
preservation,
PRL(148), 2021, pp. 1-6.
Elsevier DOI
2107
Anomaly detection, Support vector data description,
Deep learning, Autoencoder
BibRef
Lesouple, J.[Julien],
Baudoin, C.[Cédric],
Spigai, M.[Marc],
Tourneret, J.Y.[Jean-Yves],
Generalized isolation forest for anomaly detection,
PRL(149), 2021, pp. 109-119.
Elsevier DOI
2108
Anomaly detection, Isolation forest
BibRef
Li, L.[Lu],
Li, W.[Wei],
Du, Q.[Qian],
Tao, R.[Ran],
Low-Rank and Sparse Decomposition With Mixture of Gaussian for
Hyperspectral Anomaly Detection,
Cyber(51), No. 9, September 2021, pp. 4363-4372.
IEEE DOI
2109
Detectors, Hyperspectral imaging, Anomaly detection,
Matrix decomposition, Robustness, Mathematical model,
mixture of Gaussian (MoG)
BibRef
Gafni, T.[Tomer],
Cohen, K.[Kobi],
Zhao, Q.[Qing],
Searching for Unknown Anomalies in Hierarchical Data Streams,
SPLetters(28), 2021, pp. 1774-1778.
IEEE DOI
2109
Task analysis, Search problems, Complexity theory,
Inference algorithms, Computational modeling, Testing,
sequential design of experiments
BibRef
Yang, Y.X.[Yi-Xin],
Song, S.Z.[Shang-Zhen],
Liu, D.L.[De-Lian],
Zhang, J.Q.[Jian-Qi],
Chan, J.C.W.[Jonathan Cheung-Wai],
Robust Background Feature Extraction Through Homogeneous Region-Based
Joint Sparse Representation for Hyperspectral Anomaly Detection,
GeoRS(59), No. 10, October 2021, pp. 8723-8737.
IEEE DOI
2109
Dictionaries, Detectors, Feature extraction,
Sparse matrices, Interference, Anomaly detection (AD),
spectral-spatial characteristics
BibRef
Liu, S.[Senhao],
Zhang, L.[Lifu],
Cen, Y.[Yi],
Chen, L.[Likun],
Wang, Y.[Yibo],
A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy
Bilateral Smoothing and Extended Multi-Attribute Profile,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Zhao, G.P.[Gen-Ping],
Li, F.[Fei],
Zhang, X.[Xiuwei],
Laakso, K.[Kati],
Chan, J.C.W.[Jonathan Cheung-Wai],
Archetypal Analysis and Structured Sparse Representation for
Hyperspectral Anomaly Detection,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Fu, X.Y.[Xi-You],
Jia, S.[Sen],
Zhuang, L.[Lina],
Xu, M.[Meng],
Zhou, J.[Jun],
Li, Q.Q.[Qing-Quan],
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IEEE DOI
2111
Anomaly detection, Detectors, Hyperspectral imaging, Dictionaries,
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IEEE DOI
2112
Image reconstruction, Hyperspectral imaging, Anomaly detection,
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Elsevier DOI
2201
Image anomaly detection, Image anomaly localization, Density estimation
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Meta-Pixel-Driven Embeddable Discriminative Target and Background
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Elsevier DOI
2202
Anomaly detection, -nearest neighborhood algorithm,
Mini-batch -means algorithm, Metric learning, Anomaly synthesis
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Siamese Network Ensembles for Hyperspectral Target Detection with
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2203
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Li, Z.W.[Zhong-Wei],
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2205
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Hu, X.[Xing],
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IEEE DOI
2207
Manifolds, Anomaly detection, Neural networks, Laplace equations,
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Anomaly detection, Aggregation-distillation mechanism,
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geophysical image processing, hyperspectral imaging,
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Anomaly detection, Data models, Detectors, Hyperspectral imaging,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unsupervised Clustering, Classification, Unsupervised Learning .