Chang, C.I.,
Sun, T.L.,
Althouse, M.L.G.,
Unsupervised Interference Rejection Approach To Target Detection
and Classification for Hyperspectral Imagery,
OptEng(37), No. 3, March 1998, pp. 735-743.
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BibRef
Du, Q.,
Chang, C.I.,
A Signal-Decomposed and Interference-Annihilated Approach to
Hyperspectral Target Detection,
GeoRS(42), No. 4, April 2004, pp. 892-906.
IEEE Abstract.
0407
BibRef
Chang, C.I.,
Li, Y.,
Recursive Band Processing of Automatic Target Generation Process for
Finding Unsupervised Targets in Hyperspectral Imagery,
GeoRS(54), No. 9, September 2016, pp. 5081-5094.
IEEE DOI
1609
geophysical image processing
BibRef
Fu, X.P.[Xian-Ping],
Shang, X.D.[Xiao-Di],
Sun, X.D.[Xu-Dong],
Yu, H.Y.[Hao-Yang],
Song, M.P.[Mei-Ping],
Chang, C.I.[Chein-I],
Underwater Hyperspectral Target Detection with Band Selection,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Shang, X.D.[Xiao-Di],
Song, M.P.[Mei-Ping],
Wang, Y.L.[Yu-Lei],
Yu, C.Y.[Chun-Yan],
Yu, H.Y.[Hao-Yang],
Li, F.[Fang],
Chang, C.I.[Chein-I],
Target-Constrained Interference-Minimized Band Selection for
Hyperspectral Target Detection,
GeoRS(59), No. 7, July 2021, pp. 6044-6064.
IEEE DOI
2106
Object detection, Hyperspectral imaging, Signal to noise ratio,
Computer science, Interference, Band prioritization (BP),
virtual dimensionality (VD)
BibRef
Capobianco, L.,
Garzelli, A.,
Camps-Valls, G.,
Target Detection With Semisupervised Kernel Orthogonal Subspace
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GeoRS(47), No. 11, November 2009, pp. 3822-3833.
IEEE DOI
0911
BibRef
Prasad, S.,
Bruce, L.M.,
Decision Fusion With Confidence-Based Weight Assignment for
Hyperspectral Target Recognition,
GeoRS(46), No. 5, May 2008, pp. 1448-1456.
IEEE DOI
0804
BibRef
McGwire, K.C.,
Minor, T.B.,
Schultz, B.W.,
Progressive Discrimination: An Automatic Method for Mapping Individual
Targets in Hyperspectral Imagery,
GeoRS(49), No. 7, July 2011, pp. 2674-2685.
IEEE DOI
1107
BibRef
Gholizadeh, H.[Hamed],
Valadan Zoej, M.J.[Mohammad Javad],
Mojaradi, B.[Barat],
A Decision Fusion Framework for Hyperspectral Subpixel Target Detection,
PFG(2012), No. 3, 2012, pp. 267-280.
WWW Link.
1211
BibRef
Willett, R.M.,
Duarte, M.F.,
Davenport, M.A.,
Baraniuk, R.G.,
Sparsity and Structure in Hyperspectral Imaging:
Sensing, Reconstruction, and Target Detection,
SPMag(31), No. 1, January 2014, pp. 116-126.
IEEE DOI
1403
geophysical image processing
BibRef
Acito, N.[Nicola],
Diani, M.[Marco],
Mitigating the impact of signal-dependent noise on
hyperspectral target detection,
SPIE(Newsroom), September 18, 2014.
DOI Link
1410
Noise Removal. A pre-processing procedure can diminish the data noise from
new-generation hyperspectral sensors, thus minimizing negative impacts
on target detection algorithms.
BibRef
Axelsson, M.[Maria],
Friman, O.[Ola],
Haavardsholm, T.V.[Trym Vegard],
Renhorn, I.[Ingmar],
Target detection in hyperspectral imagery using forward modeling and
in-scene information,
PandRS(119), No. 1, 2016, pp. 124-134.
Elsevier DOI
1610
Hyperspectral imaging
BibRef
Zou, Z.X.[Zheng-Xia],
Shi, Z.W.[Zhen-Wei],
Hierarchical Suppression Method for Hyperspectral Target Detection,
GeoRS(54), No. 1, January 2016, pp. 330-342.
IEEE DOI
1601
hyperspectral imaging
BibRef
Liu, Y.,
Gao, G.,
Gu, Y.,
Tensor Matched Subspace Detector for Hyperspectral Target Detection,
GeoRS(55), No. 4, April 2017, pp. 1967-1974.
IEEE DOI
1704
geophysical image processing
BibRef
Wang, Z.[Ziyu],
Zhu, R.[Rui],
Fukui, K.[Kazuhiro],
Xue, J.H.[Jing-Hao],
Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case
Studies for Hyperspectral Target Detection,
IP(26), No. 11, November 2017, pp. 5447-5461.
IEEE DOI
1709
Bayes methods, Hyperspectral imaging,
Object detection, cone representation.
BibRef
Zhang, Y.X.[Yu-Xiang],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Liu, T.L.[Tong-Liang],
Joint Sparse Representation and Multitask Learning for Hyperspectral
Target Detection,
GeoRS(55), No. 2, February 2017, pp. 894-906.
IEEE DOI
1702
geophysical image processing
See also Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information.
BibRef
Wu, C.[Chen],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Hyperspectral anomalous change detection based on joint sparse
representation,
PandRS(146), 2018, pp. 137-150.
Elsevier DOI
1812
Anomalous change detection, Hyperspectral image,
Joint sparse representation, Viareggio
BibRef
Chen, Z.[Zehao],
Yang, B.[Bin],
Wang, B.[Bin],
A Preprocessing Method for Hyperspectral Target Detection Based on
Tensor Principal Component Analysis,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
BibRef
Zhang, X.R.[Xiao-Rong],
Pan, Z.B.[Zhi-Bin],
Hu, B.L.[Bing-Liang],
Zheng, X.[Xi],
Liu, W.H.[Wei-Hua],
Target detection of hyperspectral image based on spectral saliency,
IET-IPR(13), No. 2, February 2019, pp. 316-322.
DOI Link
1902
BibRef
Qi, J.H.[Jia-Hao],
Wan, P.C.[Peng-Cheng],
Gong, Z.Q.[Zhi-Qiang],
Xue, W.[Wei],
Yao, A.H.[Ai-Huan],
Liu, X.Y.[Xing-Yue],
Zhong, P.[Ping],
A Self-Improving Framework for Joint Depth Estimation and Underwater
Target Detection from Hyperspectral Imagery,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Macfarlane, F.[Fraser],
Murray, P.[Paul],
Marshall, S.[Stephen],
White, H.[Henry],
Investigating the Effects of a Combined Spatial and Spectral
Dimensionality Reduction Approach for Aerial Hyperspectral Target
Detection Applications,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Shi, Y.Z.[Yan-Zi],
Li, J.J.[Jiao-Jiao],
Li, Y.S.[Yun-Song],
Du, Q.[Qian],
Sensor-Independent Hyperspectral Target Detection With Semisupervised
Domain Adaptive Few-Shot Learning,
GeoRS(59), No. 8, August 2021, pp. 6894-6906.
IEEE DOI
2108
Object detection, Hyperspectral imaging, Feature extraction,
Adaptation models, Sensors, Task analysis,
sensor-independent hyperspectral target detection (SIHTD)
BibRef
Zhu, D.H.[De-Hui],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Two-Stream Convolutional Networks for Hyperspectral Target Detection,
GeoRS(59), No. 8, August 2021, pp. 6907-6921.
IEEE DOI
2108
Hyperspectral imaging, Object detection, Detectors, Training,
Dictionaries, Convolutional neural networks,
two-stream networks
BibRef
Gao, Y.L.[Yan-Long],
Feng, Y.[Yan],
Yu, X.[Xumin],
Hyperspectral Target Detection with an Auxiliary Generative
Adversarial Network,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Zhao, C.H.[Chun-Hui],
Wang, M.X.[Ming-Xing],
Feng, S.[Shou],
Su, N.[Nan],
Hyperspectral Target Detection Method Based on Nonlocal
Self-Similarity and Rank-1 Tensor,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI
2112
Tensors, Hyperspectral imaging, Object detection, Training,
Dictionaries, Algebra, Support vector machines, tensor product
BibRef
Chen, B.[Bowen],
Liu, L.Q.[Li-Qin],
Zou, Z.X.[Zheng-Xia],
Shi, Z.W.[Zhen-Wei],
Target Detection in Hyperspectral Remote Sensing Image:
Current Status and Challenges,
RS(15), No. 13, 2023, pp. 3223.
DOI Link
2307
BibRef
Ji, L.[Luyan],
Geng, X.R.[Xiu-Rui],
Hyperspectral Target Detection Methods Based on Statistical
Information: The Key Problems and the Corresponding Strategies,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Jiao, C.Z.[Chang-Zhe],
Chen, C.[Chao],
Gou, S.P.[Shui-Ping],
Wang, X.X.[Xiu-Xiu],
Yang, B.[Bo],
Chen, X.Y.[Xiao-Ying],
Jiao, L.C.[Li-Cheng],
L_1 Sparsity-Regularized Attention Multiple-Instance Network for
Hyperspectral Target Detection,
Cyber(53), No. 1, January 2023, pp. 124-137.
IEEE DOI
2301
Hyperspectral imaging, Object detection, Task analysis,
Neural networks, Feature extraction, Training, target detection
BibRef
Qin, H.N.[Hao-Nan],
Xie, W.Y.[Wei-Ying],
Li, Y.S.[Yun-Song],
Jiang, K.[Kai],
Lei, J.[Jie],
Du, Q.[Qian],
Weakly supervised adversarial learning via latent space for
hyperspectral target detection,
PR(135), 2023, pp. 109125.
Elsevier DOI
2212
Hyperspectral image, Target detection,
Weakly supervised learning, Adversarial learning, Latent space
BibRef
Ahmad, O.[Ola],
Collet, C.[Christophe],
Salzenstein, F.[Fabien],
Spatio-spectral Gaussian random field modeling approach for target
detection on hyperspectral data obtained in very low SNR,
ICIP15(2090-2094)
IEEE DOI
1512
Expected Euler-characteristic
BibRef
Pieper, M.,
Manolakis, D.,
Truslow, E.,
Cooley, T.,
Lipson, S.,
Performance evaluation of cluster-based hyperspectral target detection
algorithms,
ICIP12(2669-2672).
IEEE DOI
1302
BibRef
Liu, L.[Liu],
Shi, Z.W.[Zhen-Wei],
Yang, S.[Shuo],
Zhang, H.H.[Hao-Han],
Robust high-order matched filter for hyperspectral target detection
with quasi-Newton method,
CVRS12(63-66).
IEEE DOI
1302
BibRef
Krishnamurthy, K.[Kalyani],
Raginsky, M.[Maxim],
Willett, R.M.[Rebecca M.],
Hyperspectral target detection from incoherent projections:
Nonequiprobable targets and inhomogeneous SNR,
ICIP10(1357-1360).
IEEE DOI
1009
BibRef
Li, X.K.[Xiao-Kun],
Detecting subpixel targets in Hyperspectral images via knowledgeaided
adaptive filtering,
ICIP10(1365-1368).
IEEE DOI
1009
BibRef
Schaum, A.P.,
Autonomous Hyperspectral Target Detection with Quasi-Stationarity
Violation at Background Boundaries,
AIPR06(16-16).
IEEE DOI
0610
BibRef
Earlier:
Hyperspectral detection algorithms: operational, next generation, on
the horizon,
AIPR05(72-80).
IEEE DOI
0510
BibRef
Earlier:
Matched affine joint subspace detection in remote hyperspectral
reconnaissance,
AIPR02(13-18).
IEEE DOI
0210
BibRef
Schaum, A.P.,
Bayesian solutions to non-Bayesian detection problems:
Unification through fusion,
AIPR14(1-4)
IEEE DOI
1504
Bayes methods
BibRef
Schaum, A.P.,
Data association for fusion in spatial and spectral imaging,
AIPR03(87-92).
IEEE DOI
0310
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data, Endmember Extraction .