14.2.2.4 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification

Qiu, F.[Fang],
Neuro-fuzzy Based Analysis of Hyperspectral Imagery,
PhEngRS(74), No. 10, October 2008, pp. 1235-1248.
WWW Link. 0804
A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization, was developed to efficiently and effectively analyze hyperspectral data. BibRef

Zhang, Q.A.[Qi-Ang], Wang, H.[Han], Plemmons, R.J.[Robert J.], Pauca, V.P.[V. Paul],
Tensor methods for hyperspectral data analysis: A space object material identification study,
JOSA-A(25), No. 12, December 2008, pp. 3001-3012.
WWW Link. 0804
BibRef

Liu, X.W.[Xiu-Wen], Zhang, Q.A.[Qi-Ang],
Spectral histogram representations for visual modeling,
AIPR03(199-204).
IEEE DOI 0310
BibRef

Chen, J., Jia, X., Yang, W., Matsushita, B.,
Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures,
GeoRS(47), No. 7, July 2009, pp. 2165-2171.
IEEE DOI 0906
BibRef

Bellucci, J.P., Smetek, T.E., Bauer, K.W.,
Improved Hyperspectral Image Processing Algorithm Testing Using Synthetic Imagery and Factorial Designed Experiments,
GeoRS(48), No. 3, March 2010, pp. 1211-1223.
IEEE DOI 1003
BibRef

Kalluri, H.R., Prasad, S., Bruce, L.M.,
Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification,
GeoRS(48), No. 11, November 2010, pp. 4047-4058.
IEEE DOI 1011
BibRef

Bue, B.D., Merenyi, E., Csatho, B.,
Automated Labeling of Materials in Hyperspectral Imagery,
GeoRS(48), No. 11, November 2010, pp. 4059-4070.
IEEE DOI 1011
BibRef

Cao, G., Bachega, L.R., Bouman, C.A.,
The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals,
IP(20), No. 3, March 2011, pp. 625-640.
IEEE DOI 1103
BibRef

Moudden, Y., Bobin, J.,
Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints,
IP(20), No. 3, March 2011, pp. 872-879.
IEEE DOI 1103
BibRef

Plaza, A.[Antonio], Plaza, J.[Javier], Paz, A., Sanchez, S.,
Parallel Hyperspectral Image and Signal Processing,
SPMag(28), No. 3, 2011, pp. 119-126.
IEEE DOI 1105
Applications Corner BibRef

Mianji, F.A.[Fereidoun A.], Zhang, Y.[Ye],
Robust Hyperspectral Classification Using Relevance Vector Machine,
GeoRS(49), No. 6, June 2011, pp. 2100-2112.
IEEE DOI 1106
BibRef
Earlier:
Improved hyperspectral land-cover analysis using relevance vector machine,
ICIP10(2281-2284).
IEEE DOI 1009
BibRef

Chen, Y.[Yi], Nasrabadi, N.M.[Nasser M.], Tran, T.D.[Trac D.],
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation,
GeoRS(49), No. 10, October 2011, pp. 3973-3985.
IEEE DOI 1110
BibRef

Chen, Y.[Yi], Nasrabadi, N.M.[Nasser M.], Tran, T.D.[Trac D.],
Hyperspectral Image Classification via Kernel Sparse Representation,
GeoRS(51), No. 1, January 2013, pp. 217-231.
IEEE DOI 1301
BibRef
And: ICIP11(1233-1236).
IEEE DOI 1201
BibRef

Gonzalez, C., Mozos, D., Resano, J., Plaza, A.,
FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis,
GeoRS(50), No. 2, February 2012, pp. 374-388.
IEEE DOI 1201
BibRef

Li, C.[Cong], Gao, L.[Lianru], Plaza, A.[Antonio], Zhang, B.[Bing],
FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images,
RealTimeIP(16), No. 5, October 2019, pp. 1681-1694.
WWW Link. 1911
BibRef

Blanes, I., Serra-Sagrista, J., Marcellin, M.W., Bartrina-Rapesta, J.,
Divide-and-Conquer Strategies for Hyperspectral Image Processing: A Review of Their Benefits and Advantages,
SPMag(29), No. 3, 2012, pp. 71-81.
IEEE DOI 1204
BibRef

Bajorski, P.,
Generalized Detection Fusion for Hyperspectral Images,
GeoRS(50), No. 4, April 2012, pp. 1199-1205.
IEEE DOI 1204
BibRef

Sami ul Haq, Q., Tao, L., Sun, F., Yang, S.,
A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples,
GeoRS(50), No. 6, June 2012, pp. 2287-2302.
IEEE DOI 1205
BibRef

Roscher, R., Waske, B., Forstner, W.,
Incremental Import Vector Machines for Classifying Hyperspectral Data,
GeoRS(50), No. 9, September 2012, pp. 3463-3473.
IEEE DOI 1209
BibRef

Velasco-Forero, S.[Santiago], Angulo, J.[Jesus],
Classification of hyperspectral images by tensor modeling and additive morphological decomposition,
PR(46), No. 2, February 2013, pp. 566-577.
Elsevier DOI 1210
Hyperspectral images; Mathematical morphology; Pixelwise classification; Tensor modeling BibRef

Schmidt, K.[Kai],
Analyse hyperspektraler Signaturen mit doppelten Weibull-Funktionen,
PFG(2011), No. 5, 2011, pp. 349-359.
WWW Link. 1211
BibRef

Forzieri, G.[Giovanni], Moser, G.[Gabriele], Catani, F.[Filippo],
Assessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification,
PandRS(74), No. 1, November 2012, pp. 175-184.
Elsevier DOI 1212
Hyperspectral; MIVIS; Classification; Feature reduction; Complex land covers/uses BibRef

Bai, J., Xiang, S., Pan, C.,
A Graph-Based Classification Method for Hyperspectral Images,
GeoRS(51), No. 2, February 2013, pp. 803-817.
IEEE DOI 1302
BibRef

Gurram, P., Kwon, H.,
Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems,
GeoRS(51), No. 2, February 2013, pp. 787-802.
IEEE DOI 1302
Cited by 1 BibRef

Lopez, S., Vladimirova, T., Gonzalez, C., Resano, J., Mozos, D., Plaza, A.,
The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends,
PIEEE(100), No. 3, March 2013, pp. 698-722.
IEEE DOI 1303
BibRef

Lin, T.[Tao], Bourennane, S.,
Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor,
GeoRS(51), No. 6, 2013, pp. 3529-3541.
IEEE DOI 1307
hyperspectral imaging; wavelet packet transform BibRef

Rajwade, A.[Ajit], Kittle, D.[David], Tsai, T.H.[Tsung-Han], Brady, D.[David], Carin, L.[Lawrence],
Coded Hyperspectral Imaging and Blind Compressive Sensing,
SIIMS(6), No. 2, 2013, pp. 782-812.
DOI Link 1307
BibRef

Howle, C.[Christopher], Clewes, R.[Rhea], Guicheteau, J.[Jason], Ruxton, K.[Keith], Malcolm, G.[Graeme],
Imager locates toxic liquids at stand-off distances,
SPIE(Newsroom), May 15, 2013
DOI Link 1308
A novel hyperspectral imaging system can locate liquid chemical warfare agents at stand-off distances, improving operator safety and enabling the rapid survey of scenes. BibRef

Golbabaee, M., Arberet, S., Vandergheynst, P.,
Compressive Source Separation: Theory and Methods for Hyperspectral Imaging,
IP(22), No. 12, 2013, pp. 5096-5110.
IEEE DOI 1312
compressed sensing BibRef

Salas, E.A.L.[Eric Ariel L.], Henebry, G.M.[Geoffrey M.],
A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method,
RS(6), No. 1, 2013, pp. 20-41.
DOI Link 1402
BibRef

He, Z., Wang, Q., Shen, Y., Sun, M.,
Kernel Sparse Multitask Learning for Hyperspectral Image Classification With Empirical Mode Decomposition and Morphological Wavelet-Based Features,
GeoRS(52), No. 8, August 2014, pp. 5150-5163.
IEEE DOI 1403
Feature extraction BibRef

Li, L.W.[Li-Wei], Zhang, B.[Bing], Li, W.[Wei], Gao, L.R.[Lian-Ru],
Orthogonal polynomial function fitting for hyperspectral data representation and discrimination,
PRL(83, Part 2), No. 1, 2016, pp. 160-168.
Elsevier DOI 1609
Orthogonal polynomial function BibRef

Kuester, T., Spengler, D., Barczi, J.F., Segl, K., Hostert, P., Kaufmann, H.,
Simulation of Multitemporal and Hyperspectral Vegetation Canopy Bidirectional Reflectance Using Detailed Virtual 3-D Canopy Models,
GeoRS(52), No. 4, April 2014, pp. 2096-2108.
IEEE DOI 1403
crops BibRef

Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A.[J. Atli],
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods,
SPMag(31), No. 1, January 2014, pp. 45-54.
IEEE DOI 1403
computer vision BibRef

Manolakis, D., Truslow, E., Pieper, M., Cooley, T., Brueggeman, M.,
Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms,
SPMag(31), No. 1, January 2014, pp. 24-33.
IEEE DOI 1403
Survey, Object Detection. hyperspectral imaging BibRef

Gao, Y., Ji, R.R., Cui, P., Dai, Q.H., Hua, G.,
Hyperspectral Image Classification Through Bilayer Graph-Based Learning,
IP(23), No. 7, July 2014, pp. 2769-2778.
IEEE DOI 1407
BibRef

Gao, Y.[Yue], Ji, R.R.[Rong-Rong], Liu, W.[Wei], Dai, Q.H.[Qiong-Hai], Hua, G.[Gang],
Weakly Supervised Visual Dictionary Learning by Harnessing Image Attributes,
IP(23), No. 12, December 2014, pp. 5400-5411.
IEEE DOI 1412
dictionaries BibRef

Li, J.[Jun], Huang, X.[Xin], Gamba, P., Bioucas-Dias, J.M., Zhang, L.P.[Liang-Pei], Atli Benediktsson, J., Plaza, A.,
Multiple Feature Learning for Hyperspectral Image Classification,
GeoRS(53), No. 3, March 2015, pp. 1592-1606.
IEEE DOI 1412
feature extraction BibRef

Wan, L.J.[Lun-Jun], Tang, K.[Ke], Li, M.Z.[Ming-Zhi], Zhong, Y.F.[Yan-Fei], Qin, A.K.,
Collaborative Active and Semisupervised Learning for Hyperspectral Remote Sensing Image Classification,
GeoRS(53), No. 5, May 2015, pp. 2384-2396.
IEEE DOI 1502
geophysical image processing BibRef

Cui, M.[Minshan], Prasad, S.[Saurabh],
Class-Dependent Sparse Representation Classifier for Robust Hyperspectral Image Classification,
GeoRS(53), No. 5, May 2015, pp. 2683-2695.
IEEE DOI 1502
correlation theory BibRef

Cui, M.[Minshan], Prasad, S.[Saurabh],
Sparse representation-based classification: Orthogonal least squares or orthogonal matching pursuit?,
PRL(84), No. 1, 2016, pp. 120-126.
Elsevier DOI 1612
Orthogonal least square BibRef

Tuia, D.[Devis], Flamary, R.[Rémi], Courty, N.[Nicolas],
Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions,
PandRS(105), No. 1, 2015, pp. 272-285.
Elsevier DOI 1506
Hyperspectral imaging BibRef

Courty, N.[Nicolas], Flamary, R.[Rémi], Tuia, D.[Devis], Rakotomamonjy, A.,
Optimal Transport for Domain Adaptation,
PAMI(39), No. 9, September 2017, pp. 1853-1865.
IEEE DOI 1708
BibRef
Earlier: A3, A2, A4, A1:
Multitemporal classification without new labels: A solution with optimal transport,
MultiTemp15(1-4)
IEEE DOI 1511
Data analysis, Feature extraction, Kernel, Probability density function, Probability distribution, Training, Transportation, Unsupervised domain adaptation, classification, optimal transport, transfer learning, visual adaptation. geophysical image processing BibRef

Boesche, N.K.[Nina Kristine], Rogass, C.[Christian], Lubitz, C.[Christin], Brell, M.[Maximilian], Herrmann, S.[Sabrina], Mielke, C.[Christian], Tonn, S.[Sabine], Appelt, O.[Oona], Altenberger, U.[Uwe], Kaufmann, H.[Hermann],
Hyperspectral REE (Rare Earth Element) Mapping of Outcrops: Applications for Neodymium Detection,
RS(7), No. 5, 2015, pp. 5160-5186.
DOI Link 1506
BibRef

Jia, M.[Meng], Gong, M.[Maoguo], Jiao, L.C.[Li-Cheng],
Hyperspectral image classification using discontinuity adaptive class-relative nonlocal means and energy fusion strategy,
PandRS(106), No. 1, 2015, pp. 16-27.
Elsevier DOI 1507
Change detection BibRef

Abdel-Rahman, E.M.[Elfatih M.], Makori, D.M.[David M.], Landmann, T.[Tobias], Piiroinen, R.[Rami], Gasim, S.[Seif], Pellikka, P.[Petri], Raina, S.K.[Suresh K.],
The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping,
RS(7), No. 10, 2015, pp. 13298.
DOI Link 1511
BibRef

Bannon, D.[David],
Hyperspectral, multispectral technologies find commercial applications,
SPIE(Newsroom), July 6, 2015
DOI Link 1511
First developed for military use, hyperspectral imaging now brings in-line inspection of foods and consumer products to new levels of accuracy. Its applications extend beyond the production line as well, says Headwall's CEO. BibRef

Bauer, S.[Sebastian], León, F.P.[Fernando Puente],
Hyperspectral fluorescence imaging for mineral classification,
30 July 2015, SPIE Newsroom. DOI: SPIE(Newsroom), July 30, 2015
DOI Link 1511
A novel approach can be used for industrial sorting and presents several advantages over conventional hyperspectral imaging techniques. BibRef

Peng, B.[Bing], Li, W.[Wei], Xie, X.M.[Xiao-Ming], Du, Q.[Qian], Liu, K.[Kui],
Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery,
RS(7), No. 11, 2015, pp. 14806.
DOI Link 1512
BibRef

Sidike, P., Asari, V.K., Alam, M.S.,
Multiclass Object Detection With Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation,
GeoRS(54), No. 2, February 2016, pp. 1196-1208.
IEEE DOI 1601
Correlation BibRef

Toksöz, M.A., Ulusoy, I.,
Hyperspectral Image Classification via Basic Thresholding Classifier,
GeoRS(54), No. 7, July 2016, pp. 4039-4051.
IEEE DOI 1606
Computational efficiency BibRef

Toksöz, M.A., Ulusoy, I.,
Hyperspectral Image Classification via Kernel Basic Thresholding Classifier,
GeoRS(55), No. 2, February 2017, pp. 715-728.
IEEE DOI 1702
hyperspectral imaging BibRef

Zhao, C.Y.[Chong-Yue], Gao, X.B.[Xin-Bo], Wang, Y.[Ying], Li, J.[Jie],
Efficient Multiple-Feature Learning-Based Hyperspectral Image Classification with Limited Training Samples,
GeoRS(54), No. 7, July 2016, pp. 4052-4062.
IEEE DOI 1606
Bayes methods BibRef

Xu, X.[Xiang], Li, J.[Jun], Li, S.T.[Shu-Tao],
Multiview Intensity-Based Active Learning for Hyperspectral Image Classification,
GeoRS(56), No. 2, February 2018, pp. 669-680.
IEEE DOI 1802
Feature extraction, Hyperspectral imaging, Learning systems, Prediction methods, Uncertainty, multiview intensity-based query (MVIQ) BibRef

Zehtabian, A., Ghassemian, H.,
Automatic Object-Based Hyperspectral Image Classification Using Complex Diffusions and a New Distance Metric,
GeoRS(54), No. 7, July 2016, pp. 4106-4114.
IEEE DOI 1606
Diffusion processes BibRef

Zhong, Z., Fan, B., Ding, K., Li, H., Xiang, S., Pan, C.,
Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study,
GeoRS(54), No. 8, August 2016, pp. 4461-4478.
IEEE DOI 1608
feature extraction BibRef

Wu, H., Prasad, S.,
Dirichlet Process Based Active Learning and Discovery of Unknown Classes for Hyperspectral Image Classification,
GeoRS(54), No. 8, August 2016, pp. 4882-4895.
IEEE DOI 1608
geophysical image processing BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
A Novel Synergetic Classification Approach for Hyperspectral and Panchromatic Images Based on Self-Learning,
GeoRS(54), No. 8, August 2016, pp. 4917-4928.
IEEE DOI 1608
hyperspectral imaging BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images,
RS(8), No. 10, 2016, pp. 804.
DOI Link 1609
BibRef

Lu, X.C.[Xiao-Chen], Zhang, J.P.[Jun-Ping], Li, T.[Tong], Zhang, Y.[Ye],
Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Chang, C.I.,
Spectral Inter-Band Discrimination Capacity of Hyperspectral Imagery,
GeoRS(56), No. 3, March 2018, pp. 1749-1766.
IEEE DOI 1804
hyperspectral imaging, image processing, probability, remote sensing, spectral analysis, BS -band subset, band selection, virtual dimensionality (VD) BibRef

Peralta, B.[Billy], Caro, A.[Alberto], Soto, A.[Alvaro],
A proposal for supervised clustering with Dirichlet Process using labels,
PRL(80), No. 1, 2016, pp. 52-57.
Elsevier DOI 1609
Dirichlet Process BibRef

Ma, X.R.[Xiao-Rui], Wang, H.Y.[Hong-Yu], Wang, J.[Jie],
Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning,
PandRS(120), No. 1, 2016, pp. 99-107.
Elsevier DOI 1610
Hyperspectral image BibRef

Arablouei, R., de Hoog, F.,
Hyperspectral Image Recovery via Hybrid Regularization,
IP(25), No. 12, December 2016, pp. 5649-5663.
IEEE DOI 1612
convergence of numerical methods BibRef

Xia, J.S.[Jun-Shi], Yokoya, N.[Naoto], Iwasaki, A.[Akira],
Hyperspectral Image Classification With Canonical Correlation Forests,
GeoRS(55), No. 1, January 2017, pp. 421-431.
IEEE DOI 1701
Markov processes BibRef

Xia, J.S.[Jun-Shi], Ghamisi, P., Yokoya, N.[Naoto], Iwasaki, A.[Akira],
Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification,
GeoRS(56), No. 1, January 2018, pp. 202-216.
IEEE DOI 1801
Boosting, Feature extraction, Hyperspectral imaging, Radio frequency, Sensors, Vegetation, Ensemble learning, random forest (RF) BibRef

Li, L.[Lu], Wang, C.Y.[Cheng-Yi], Chen, J.B.[Jing-Bo], Ma, J.L.[Jiang-Lin],
Refinement of Hyperspectral Image Classification with Segment-Tree Filtering,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Zhu, W., Chayes, V., Tiard, A., Sanchez, S., Dahlberg, D., Bertozzi, A.L., Osher, S., Zosso, D., Kuang, D.,
Unsupervised Classification in Hyperspectral Imagery With Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm,
GeoRS(55), No. 5, May 2017, pp. 2786-2798.
IEEE DOI 1705
geophysical image processing, hyperspectral imaging, image classification, pattern clustering, Merriman-Bence-Osher scheme, graph based nonlocal total variation method, hyperspectral imagery, hyperspectral images, labeling function, primal dual hybrid gradient algorithm, random initialization, stable simplex clustering routine, unsupervised classification, unsupervised clustering method, variational problem, Clustering algorithms, Clustering methods, Hyperspectral imaging, Image processing, Labeling, Sparse matrices, TV, Hyperspectral images (HSI), nonlocal total variation (NLTV), primal-dual hybrid gradient (PDHG) algorithm, stable simplex clustering, unsupervised, classification BibRef

Wang, Z.M.[Zeng-Mao], Du, B.[Bo], Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Jia, X.P.[Xiu-Ping],
A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification,
GeoRS(55), No. 6, June 2017, pp. 3071-3083.
IEEE DOI 1706
Hyperspectral imaging, Labeling, Semisupervised learning, Training, Uncertainty, Active learning, hyperspectral classification, semisupervised, learning BibRef

Yang, J.Q.[Jia-Qi], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
From center to surrounding: An interactive learning framework for hyperspectral image classification,
PandRS(197), 2023, pp. 145-166.
Elsevier DOI 2303
Hyperspectral image classification, Deep learning, Center-to-surrounding interactive learning, Transformer BibRef

Yang, J.Q.[Jia-Qi], Du, B.[Bo], Wang, D.[Di], Zhang, L.P.[Liang-Pei],
ITER: Image-to-Pixel Representation for Weakly Supervised HSI Classification,
IP(33), 2024, pp. 257-272.
IEEE DOI 2312
BibRef

Yin, J., Qv, H., Luo, X., Jia, X.,
Segment-Oriented Depiction and Analysis for Hyperspectral Image Data,
GeoRS(55), No. 7, July 2017, pp. 3982-3996.
IEEE DOI 1706
BibRef
And: Corrections: GeoRS(56), No. 2, February 2018, pp. 1213-1213.
IEEE DOI 1802
Dictionaries, Encoding, Hyperspectral imaging, Image segmentation, Matching pursuit algorithms, Training, Dictionary learning, hyperspectral image (HSI) classification, segment, sparse, representation BibRef

Qv, H., Yin, J., Luo, X., Jia, X.,
Band Dual Density Discrimination Analysis for Hyperspectral Image Classification,
GeoRS(56), No. 12, December 2018, pp. 7257-7271.
IEEE DOI 1812
Hyperspectral imaging, Indexes, Task analysis, Correlation, Dimensionality reduction, Feature extraction, hyperspectral image (HSI) classification BibRef

Pan, B.[Bin], Shi, Z.W.[Zhen-Wei], Xu, X.[Xia], Yang, Y.[Yi],
Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Gao, L.[Lianru], Zhao, B.[Bin], Jia, X.P.[Xiu-Ping], Liao, W.Z.[Wen-Zhi], Zhang, B.[Bing],
Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Prabhakar, T.V.N.[T.V. Nidhin], Geetha, P.,
Two-dimensional empirical wavelet transform based supervised hyperspectral image classification,
PandRS(133), No. Supplement C, 2017, pp. 37-45.
Elsevier DOI 1711
Image empirical mode decomposition, Empirical wavelet transform, Hyperspectral image classification, Feature extraction, Subspace pursuit, Orthogonal matching pursuit, Support vector machines BibRef

Tong, F.[Fei], Tong, H.J.[Heng-Jian], Jiang, J.J.[Jun-Jun], Zhang, Y.[Yun],
Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Leng, Q.M.[Qing-Ming], Yang, H.[Haiou], Jiang, J.J.[Jun-Jun], Tian, Q.[Qi],
Adaptive MultiScale Segmentations for Hyperspectral Image Classification,
GeoRS(58), No. 8, August 2020, pp. 5847-5860.
IEEE DOI 2007
Indexes, Image segmentation, Hyperspectral imaging, Complexity theory, Image color analysis, Support vector machines, multiscale segmentations BibRef

Hyperspectral imaging: defense technology transfers into commercial applications,
SPIE(Newsroom), October 9, 2017.
HTML Version. 1711
Photonics for a Better World. Hyperspectral imaging, like many other of today's technologies, is moving into numerous commercial markets after developing and maturing in the defense sector. BibRef

Yang, J.L.[Jun-Li], Jiang, Z.G.[Zhi-Guo], Hao, S.A.[Shu-Ang], Zhang, H.P.[Hao-Peng],
Higher Order Support Vector Random Fields for Hyperspectral Image Classification,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801
BibRef

Zhang, X., Gao, Z., Jiao, L., Zhou, H.,
Multifeature Hyperspectral Image Classification With Local and Nonlocal Spatial Information via Markov Random Field in Semantic Space,
GeoRS(56), No. 3, March 2018, pp. 1409-1424.
IEEE DOI 1804
Markov processes, feature extraction, hyperspectral imaging, image classification, image segmentation, semantic representation BibRef

Li, J.J.[Jiao-Jiao], Du, Q.[Qian], Li, Y.S.[Yun-Song], Li, W.[Wei],
Hyperspectral Image Classification With Imbalanced Data Based on Orthogonal Complement Subspace Projection,
GeoRS(56), No. 7, July 2018, pp. 3838-3851.
IEEE DOI 1807
Collaboration, Feature extraction, Hyperspectral imaging, Support vector machines, Testing, Training, sparse representation BibRef

Ge, H.M.[Hai-Miao], Wang, L.G.[Li-Guo], Liu, Y.Z.[Yan-Zhong], Li, C.[Cheng], Chen, R.X.[Rui-Xin],
Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs,
IET-IPR(12), No. 6, June 2018, pp. 941-947.
DOI Link 1805
BibRef

Zu, B.K.[Bao-Kai], Xia, K.[Kewen], Du, W.[Wei], Li, Y.F.[Ya-Fang], Ali, A.[Ahmad], Chakraborty, S.[Sagnik],
Classification of Hyperspectral Images with Robust Regularized Block Low-Rank Discriminant Analysis,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Samat, A.[Alim], Gamba, P.[Paolo], Liu, S.C.[Si-Cong], Li, E.[Erzhu], Miao, Z.[Zelang], Abuduwaili, J.[Jilili],
Fuzzy multiclass active learning for hyperspectral image classification,
IET-IPR(12), No. 7, July 2018, pp. 1095-1101.
DOI Link 1806
BibRef

Rocha, A.D.[Alby D.], Groen, T.A.[Thomas A.], Skidmore, A.K.[Andrew K.], Darvishzadeh, R.[Roshanak], Willemen, L.[Louise],
Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Gao, H.M.[Hong-Min], Yang, Y.[Yao], Li, C.M.[Chen-Ming], Zhou, H.[Hui], Qu, X.Y.[Xiao-Yu],
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Deep learning, Training, Integrated optics, Inspection, Optical imaging, Robustness, Optical materials, Classification BibRef

Makantasis, K., Voulodimos, A., Doulamis, A.D., Doulamis, N.D., Georgoulas, I.,
Hyperspectral Image Classification with Tensor-Based Rank-R Learning Models,
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Hyperspectral image classification, machine learning, tensor rank decomposition BibRef

Li, S., Hao, Q., Gao, G., Kang, X.,
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GeoRS(56), No. 12, December 2018, pp. 7195-7206.
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Hyperspectral imaging, Indexes, Performance evaluation, Image color analysis, Correlation, Accuracy indexes, performance evaluation BibRef

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Total Variation Regularized Collaborative Representation Clustering With a Locally Adaptive Dictionary for Hyperspectral Imagery,
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IEEE DOI 1901
Collaboration, Dictionaries, Clustering algorithms, Clustering methods, Hyperspectral imaging, Sparse matrices, total variation (TV) BibRef

Pan, B., Shi, Z., Xu, X.,
Multiobjective-Based Sparse Representation Classifier for Hyperspectral Imagery Using Limited Samples,
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IEEE DOI 1901
Feature extraction, Dictionaries, Hyperspectral imaging, Training, Linear programming, Support vector machines, sparse representation BibRef

Jiang, J., Ma, J., Wang, Z., Chen, C., Liu, X.,
Hyperspectral Image Classification in the Presence of Noisy Labels,
GeoRS(57), No. 2, February 2019, pp. 851-865.
IEEE DOI 1901
Hyperspectral imaging, Noise measurement, Training, Databases, Noise level, Radio frequency, Hyperspectral image classification, superpixel segmentation BibRef

Huang, N.[Nan], Xiao, L.[Liang],
Hyperspectral image clustering via sparse dictionary-based anchored regression,
IET-IPR(13), No. 2, February 2019, pp. 261-269.
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Hypergraph p-Laplacian Regularization for Remotely Sensed Image Recognition,
GeoRS(57), No. 3, March 2019, pp. 1585-1595.
IEEE DOI 1903
approximation theory, graph theory, image recognition, regression analysis, remote sensing, statistical distributions, p-Laplacian BibRef

Chang, C.,
Statistical Detection Theory Approach to Hyperspectral Image Classification,
GeoRS(57), No. 4, April 2019, pp. 2057-2074.
IEEE DOI 1904
Bayes methods, feature extraction, hyperspectral imaging, image classification, matrix algebra, precision rate (PR) BibRef

Windrim, L.[Lloyd], Ramakrishnan, R.[Rishi], Melkumyan, A.[Arman], Murphy, R.J.[Richard J.], Chlingaryan, A.[Anna],
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Windrim, L.[Lloyd], Melkumyan, A.[Arman], Murphy, R.J.[Richard J.], Chlingaryan, A.[Anna], Nieto, J.,
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ICIP16(4453-4457)
IEEE DOI 1610
Atmospheric modeling BibRef

Pan, C.[Chao], Gao, X.B.[Xin-Bo], Wang, Y.[Ying], Li, J.[Jie],
Markov Random Fields Integrating Adaptive Interclass-Pair Penalty and Spectral Similarity for Hyperspectral Image Classification,
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IEEE DOI 1905
geophysical image processing, graph theory, hyperspectral imaging, image classification, image segmentation, spectral similarity BibRef

Pan, C.[Chao], Jia, X.P.[Xiu-Ping], Li, J.[Jie], Gao, X.B.[Xin-Bo],
Adaptive Edge Preserving Maps in Markov Random Fields for Hyperspectral Image Classification,
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Feature extraction, Support vector machines, Labeling, Image edge detection, Kernel, Image segmentation, Markov random fields (MRFs) BibRef

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Zhou, P.C.[Pei-Cheng], Han, J.W.[Jun-Wei], Cheng, G.[Gong], Zhang, B.C.[Bao-Chang],
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GeoRS(57), No. 7, July 2019, pp. 4823-4833.
IEEE DOI 1907
Feature extraction, Training, Hyperspectral imaging, Neurons, Kernel, Deep learning, Discriminative stacked autoencoder (DSAE), local Fisher discriminative regularization BibRef

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Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range,
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Bazine, R.[Razika], Wu, H.Y.[Hua-Yi], Boukhechba, K.[Kamel],
Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification,
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Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.,
Deep Learning for Hyperspectral Image Classification: An Overview,
GeoRS(57), No. 9, September 2019, pp. 6690-6709.
IEEE DOI 1909
Feature extraction, Deep learning, Hyperspectral imaging, Training, Logistics, Classification, deep learning, feature extraction, hyperspectral image (HSI) BibRef

Liu, X., Wang, R., Cai, Z., Cai, Y., Yin, X.,
Deep Multigrained Cascade Forest for Hyperspectral Image Classification,
GeoRS(57), No. 10, October 2019, pp. 8169-8183.
IEEE DOI 1910
geophysical image processing, hyperspectral imaging, image classification, learning (artificial intelligence), machine learning BibRef

Huang, Z.[Zehua], Chen, Q.[Qi], Chen, Q.H.[Qi-Hao], Liu, X.G.[Xiu-Guo], He, H.[Hao],
A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization,
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Bazine, R.[Razika], Wu, H.Y.[Hua-Yi], Boukhechba, K.[Kamel],
Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification,
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Xu, Q.[Qin], Xiao, Y.[Yong], Wang, D.Y.[Dong-Yue], Luo, B.[Bin],
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Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data,
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Zahidi, U.A.[Usman A.], Yuen, P.W.T.[Peter W. T.], Piper, J.[Jonathan], Godfree, P.S.[Peter S.],
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He, J.[Ji], Zhao, L.[Lina], Yang, H.W.[Hong-Wei], Zhang, M.M.[Meng-Meng], Li, W.[Wei],
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IEEE DOI 2001
Feature extraction, Bit error rate, Hyperspectral imaging, Shape, Deep learning, Kernel, Deep learning, hyperspectral image, pattern recognition BibRef

Alcolea, A.[Adrián], Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Resano, J.[Javier], Plaza, A.[Antonio],
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Hyperspectral imaging, Training, Task analysis, Manifolds, Face recognition, Indexes, Hyperspectral image, regression-based classification BibRef

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Density peak (DP) clustering, Gaussian weighting, hyperspectral images (HSI), noisy labels, support vector machines (SVMs) BibRef

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Yang, Z., Cao, F., Cheng, Y., Ling, W.K., Hu, R.,
Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images,
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IEEE DOI 2012
Feature extraction, Collaboration, Training, Testing, Robustness, Hyperspectral imaging, Coordinate information (CI), sparse representation BibRef

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Zhao, Q.H.[Quan-Hua], Jia, S.H.[Shu-Han], Li, Y.[Yu],
Hyperspectral Remote Sensing Image Classification Based on Tighter Random Projection with Minimal Intra-Class Variance Algorithm,
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Random projection, Dimensionality reduction, Image size, Minimum distance classifier, Hyperspectral remote sensing image classification BibRef

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Hyperspectral Remote Sensing Image Classification Based on Partitioned Random Projection Algorithm,
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Jia, S.H.[Shu-Han], Zhao, Q.H.[Quan-Hua], Wang, L., Li, Y.[Yu],
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Xu, Y., Wu, Z., Chanussot, J., Wei, Z.,
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Tensile stress, Image reconstruction, Imaging, Hyperspectral imaging, Matrix decomposition, Dictionaries, spectral quadratic variation BibRef

Pan, L.[Lei], He, C.X.[Cheng-Xun], Xiang, Y.[Yang], Sun, L.[Le],
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Hyperspectral image, Image classification, Auto-Encoder BibRef

He, Z.P.[Zi-Ping], Xia, K.[Kewen], Li, T.J.[Tie-Jun], Zu, B.K.[Bao-Kai], Yin, Z.X.[Zhi-Xian], Zhang, J.N.[Jiang-Nan],
A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification,
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Guo, H.F.[Hu-Feng], Liu, W.Y.[Wen-Yi],
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Yu, X.[Xumin], Feng, Y.[Yan], Gao, Y.L.[Yan-Long], Jia, Y.B.[Ying-Biao], Mei, S.H.[Shao-Hui],
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Liu, G.C.[Gui-Chi], Gao, L.[Lei], Qi, L.[Lin],
Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation,
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Gao, L.[Lei], Guo, Z.[Zheng], Guan, L.[Ling],
A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification*,
ICPR21(7595-7600)
IEEE DOI 2105
Correlation, Databases, Face recognition, Feature extraction, Classification algorithms, Data mining, Task analysis, image classification BibRef

Anand, R., Veni, S., Aravinth, J.,
Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform,
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Pan, E.[Erting], Ma, Y.[Yong], Fan, F.[Fan], Mei, X.G.[Xiao-Guang], Huang, J.[Jun],
Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories,
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Huang, B.X.[Bao-Xiang], Ge, L.Y.[Lin-Yao], Chen, G.[Ge], Radenkovic, M.[Milena], Wang, X.P.[Xiao-Peng], Duan, J.M.[Jin-Ming], Pan, Z.K.[Zhen-Kuan],
Nonlocal graph theory based transductive learning for hyperspectral image classification,
PR(116), 2021, pp. 107967.
Elsevier DOI 2106
Transductive learning, Nonlocal graph, Label propagation, Variational method, Hyperspectral image classification BibRef

Danielsen, A.S.[Aksel S.], Johansen, T.A.[Tor Arne], Garrett, J.L.[Joseph L.],
Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat,
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Shi, C.[Cheng], Fang, L.[Li], Lv, Z.Y.[Zhi-Yong], Zhao, M.H.[Ming-Hua],
Explainable scale distillation for hyperspectral image classification,
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Elsevier DOI 2112
Hyperspectral image classification, Knowledge distillation, Scale distillation, Explainable scale network BibRef

Alameddine, J.[Jihan], Chehdi, K.[Kacem], Cariou, C.[Claude],
Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images,
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Zhang, Y.I.[Youq-Iang], Cao, G.[Guo], Wang, B.S.[Bi-Sheng], Li, X.S.[Xue-Song], Amoako, P.Y.O.[Prince Yaw Owusu], Shafique, A.[Ayesha],
Dual Sparse Representation Graph-Based Copropagation for Semisupervised Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Collaboration, Hyperspectral imaging, Telecommunications, Linear programming, Support vector machines, Mathematical model, sparse representation (SR) graph BibRef

Shah, C.[Chiranjibi], Du, Q.[Qian], Xu, Y.[Yan],
Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification,
RS(14), No. 3, 2022, pp. xx-yy.
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Qing, Y.H.[Yu-Hao], Huang, Q.Z.[Quan-Zhen], Feng, L.Y.[Liu-Yan], Qi, Y.Y.[Yue-Yan], Liu, W.Y.[Wen-Yi],
Multiscale Feature Fusion Network Incorporating 3D Self-Attention for Hyperspectral Image Classification,
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Zheng, H.[Hui], Cao, Y.Z.[Yi-Zhi], Sun, M.[Min], Guo, G.H.[Gui-Hai], Meng, J.Z.[Jun-Zhen], Guo, X.W.[Xin-Wei], Jiang, Y.C.[Yan-Chi],
Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification,
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Chakraborty, S.[Saptarshi], Das, S.[Swagatam],
Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach,
PAMI(44), No. 6, June 2022, pp. 2894-2908.
IEEE DOI 2205
Clustering algorithms, Feature extraction, Clustering methods, Optimization, Noise measurement, Minimization, Context modeling, strong consistency BibRef

Gong, N.[Na], Zhang, C.L.[Chun-Lei], Zhou, H.[Heng], Zhang, K.[Kai], Wu, Z.Y.[Zhong-Yuan], Zhang, X.[Xin],
Classification of hyperspectral images via improved cycle-MLP,
IET-CV(16), No. 5, 2022, pp. 468-478.
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cycleMLP, classification of hyperspectral image, deep learning, driftFC BibRef

Yao, W.[Wei], Lian, C.[Cheng], Bruzzone, L.[Lorenzo],
A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification,
RS(14), No. 19, 2022, pp. xx-yy.
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Nguyen, T.S.[Tan-Sy], Luong, M.[Marie], Kaaniche, M.[Mounir], Ngo, L.H.[Long H.], Beghdadi, A.[Azeddine],
A novel multi-branch wavelet neural network for sparse representation based object classification,
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Elsevier DOI 2212
Object classification, Sparse coding, Wavelet transform, Neural networks, Multi-branch architecture BibRef

Ngo, L.H.[Long H.], Luong, M.[Marie], Sirakov, N.M.[Nikolay M.], Le-Tien, T.[Thuong], Guerif, S.[Sebastien], Viennet, E.[Emmanuel],
Sparse Representation Wavelet Based Classification,
ICIP18(2974-2978)
IEEE DOI 1809
Improve conventional Sparse Representation Classification. Dictionaries, Training, Discrete wavelet transforms, Face, Wavelet domain, low-pass subband BibRef

Cao, C.H.[Chun-Hong], Duan, H.X.[Hong-Xuan], Gao, X.[Xieping],
Hyperspectral image classification based on three-dimensional adaptive sampling and improved iterative shrinkage-threshold algorithm,
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Elsevier DOI 2301
Feature learning, Discriminative feature, Three-dimensional adaptive sampling, Hyperspectral images classification BibRef

Guan, J.[Junyi], Li, S.[Sheng], He, X.X.[Xiong-Xiong], Zhu, J.H.[Jin-Hui], Chen, J.J.[Jia-Jia], Si, P.[Peng],
SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak Clustering Algorithm,
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IEEE DOI 2304
Clustering algorithms, Shape, Density measurement, Clustering methods, Resource management, Periodic structures, auto-tuning BibRef

Xu, Z.[Zeyu], Su, C.[Cheng], Wang, S.[Shirou], Zhang, X.C.[Xiao-Can],
Local and Global Spectral Features for Hyperspectral Image Classification,
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Huang, S.G.[Shao-Guang], Zhang, H.Y.[Hong-Yan], Zeng, H.J.[Hai-Jin], Pižurica, A.[Aleksandra],
From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images,
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Huang, S.G.[Shao-Guang], Zhang, H.Y.[Hong-Yan], Liao, W., Pižurica, A.[Aleksandra],
Robust joint sparsity model for hyperspectral image classification,
ICIP17(3130-3134)
IEEE DOI 1803
Gaussian noise, Hyperspectral imaging, Optimization, Robustness, Sparse matrices, Training, Robust classification, super-pixel segmentation BibRef

Chen, H.Y.[Hua-Yue], Wang, T.T.[Ting-Ting], Chen, T.[Tao], Deng, W.[Wu],
Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network,
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Xie, J.X.[Jia-Xing], Hua, J.J.[Jia-Jun], Chen, S.N.[Shao-Nan], Wu, P.W.[Pei-Wen], Gao, P.[Peng], Sun, D.Z.[Dao-Zong], Lyu, Z.D.[Zhen-Dong], Lyu, S.L.[Shi-Lei], Xue, X.Y.[Xiu-Yun], Lu, J.Q.[Jian-Qiang],
HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image Classification Method for Crop Classification,
RS(15), No. 14, 2023, pp. 3491.
DOI Link 2307
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Yang, Z.[Zian], Zheng, N.R.[Nai-Rong], Wang, F.[Feng],
DSSFN: A Dual-Stream Self-Attention Fusion Network for Effective Hyperspectral Image Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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Gong, J.F.[Jin-Fu], Li, F.M.[Fan-Ming], Wang, J.[Jian], Yang, Z.Y.[Zheng-Ye], Ding, X.Z.[Xue-Zhuan],
A Split-Frequency Filter Network for Hyperspectral Image Classification,
RS(15), No. 15, 2023, pp. xx-yy.
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Li, S.Y.[Shu-Ying], Wang, S.W.[Shao-Wei], Li, Q.[Qiang],
Joint Posterior Probability Active Learning for Hyperspectral Image Classification,
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DOI Link 2309
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Liu, B.[Bing], Sun, Y.F.[Yi-Fan], Yu, A.[Anzhu], Xue, Z.X.[Zhi-Xiang], Zuo, X.B.[Xi-Bing],
Hyperspectral Meets Optical Flow: Spectral Flow Extraction for Hyperspectral Image Classification,
IP(32), 2023, pp. 5181-5196.
IEEE DOI 2310
Optical Floe. BibRef

Zhang, P.[Ping], Yu, H.Y.[Hai-Yang], Li, P.[Pengao], Wang, R.[Ruili],
TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification,
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Cui, B.[Binge], Wen, J.X.[Jia-Xiang], Song, X.[Xiukai], He, J.L.[Jian-Long],
MADANet: A Lightweight Hyperspectral Image Classification Network with Multiscale Feature Aggregation and a Dual Attention Mechanism,
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Ding, C.[Chen], Li, X.[Xu], Chen, J.Y.[Jing-Yi], Xu, Y.Y.[Yao-Yang], Zheng, M.M.[Meng-Meng], Zhang, L.[Lei],
Hyperspectral Image Classification Promotion Using Dynamic Convolution Based on Structural Re-Parameterization,
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Yang, X.F.[Xiao-Fei], Cao, W.J.[Wei-Jia], Lu, Y.[Yao], Zhou, Y.C.[Yi-Cong],
QTN: Quaternion Transformer Network for Hyperspectral Image Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7370-7384.
IEEE DOI 2312
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Fang, S.[Shaoyi], Li, X.Y.[Xin-Yu], Tian, S.[Shimao], Chen, W.H.[Wei-Hao], Zhang, E.[Erlei],
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Zhang, Z.Q.[Zhong-Qiang], Gao, D.[Dahua], Liu, D.H.[Dan-Hua], Shi, G.M.[Guang-Ming],
Spectral-Spatial Domain Attention Network for Hyperspectral Image Few-Shot Classification,
RS(16), No. 3, 2024, pp. 592.
DOI Link 2402
BibRef

Liu, Y.[Yan], Wang, X.X.[Xi-Xi], Jiang, B.[Bo], Chen, L.[Lan], Luo, B.[Bin],
SemanticFormer: Hyperspectral image classification via semantic transformer,
PRL(179), 2024, pp. 1-8.
Elsevier DOI Code:
WWW Link. 2403
Hyperspectral image (HSI) classification, Transformer, Semantic token BibRef

Zhan, Y.[Ying], Hu, D.[Dan], Yu, X.[Xianchuan], Wang, Y.F.[Yu-Feng],
Hyperspectral Image Classification Based on Mutually Guided Image Filtering,
RS(16), No. 5, 2024, pp. 870.
DOI Link 2403
BibRef

Liao, Q.M.[Qi-Ming], Zhao, L.[Lin], Luo, W.Q.[Wen-Qiang], Li, X.P.[Xin-Ping], Zhang, G.[Guoyun],
Joint negative-positive-learning based sample reweighting for hyperspectral image classification with label noise,
PRL(183), 2024, pp. 98-103.
Elsevier DOI 2406
Hyperspectral image (HSI), Meta learning, Joint positive and negative learning (JPNL), Label-noise BibRef

Yang, C.L.[Chun-Lan], Kong, Y.[Yi], Wang, X.S.[Xue-Song], Cheng, Y.[Yuhu],
Hyperspectral Image Classification Based on Adaptive Global-Local Feature Fusion,
RS(16), No. 11, 2024, pp. 1918.
DOI Link 2406
BibRef

Feng, Z.X.[Zhi-Xi], Tong, S.L.[Shi-Lin], Yang, S.Y.[Shu-Yuan], Zhang, X.Y.[Xin-Yu], Jiao, L.C.[Li-Cheng],
Pseudo-Label-Assisted Subdomain Adaptation for Hyperspectral Image Classification,
CirSysVideo(34), No. 6, June 2024, pp. 4729-4744.
IEEE DOI 2406
Training, Feature extraction, Task analysis, Hyperspectral imaging, Adaptation models, Testing, Sensors, Pseudo-label, hyperspectral image (HSI) classification BibRef

Li, G.F.[Guang-Fei], Gao, Q.X.[Quan-Xue], Han, J.G.[Jun-Gong], Gao, X.B.[Xin-Bo],
A Coarse-to-Fine Cell Division Approach for Hyperspectral Remote Sensing Image Classification,
CirSysVideo(34), No. 6, June 2024, pp. 4928-4941.
IEEE DOI 2406
Training, Feature extraction, Target recognition, Data models, Complexity theory, Semantics, Task analysis, HRSIs, classification, class specificity distribution BibRef

Zhang, M.[Meng], Yang, Y.[Yi], Zhang, S.[Sixian], Mi, P.B.[Peng-Bo], Han, D.Q.[De-Qiang],
Spectral-Spatial Center-Aware Bottleneck Transformer for Hyperspectral Image Classification,
RS(16), No. 12, 2024, pp. 2152.
DOI Link 2406
BibRef

Chen, Z.J.[Zhi-Jie], Chen, Y.[Yu], Wang, Y.[Yuan], Wang, X.Y.[Xiao-Yan], Wang, X.S.[Xin-Sheng], Xiang, Z.R.[Zhou-Ru],
DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image,
RS(16), No. 16, 2024, pp. 3002.
DOI Link 2408
BibRef

Liu, Y.[Yi], Jiang, S.[Shanjiao], Liu, Y.J.[Yi-Jin], Mu, C.H.[Cai-Hong],
Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification,
RS(16), No. 17, 2024, pp. 3124.
DOI Link 2409
BibRef

Guo, W.Q.[Wen-Qi], Xu, X.[Xu], Xu, X.Q.[Xiao-Qiang], Gao, S.C.[Shi-Chen], Wu, Z.[Zibu],
Clustering Hyperspectral Imagery via Sparse Representation Features of the Generalized Orthogonal Matching Pursuit,
RS(16), No. 17, 2024, pp. 3230.
DOI Link 2409
BibRef

Yang, X.F.[Xiao-Fei], Luo, Y.X.[Yu-Xiong], Zhang, Z.[Zhen], Tang, D.[Dong], Zhou, Z.[Zheng], Tang, H.J.[Hao-Jin],
AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification,
RS(16), No. 18, 2024, pp. 3412.
DOI Link 2410
BibRef

Zhao, H.[Hengwei], Wang, X.Y.[Xin-Yu], Li, J.T.[Jing-Tao], Zhong, Y.F.[Yan-Fei],
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery,
ICCV23(16781-16790)
IEEE DOI Code:
WWW Link. 2401
BibRef

Shi, C.[Cheng], Liu, Y.[Ying], Zhao, M.H.[Ming-Hua], You, Z.Z.[Zhen-Zhen], Zhao, Z.Y.[Zi-Yuan],
Adversarial Defense via Perturbation-Disentanglement in Hyperspectral Image Classification,
ICIP23(2935-2939)
IEEE DOI 2312
BibRef

Paheding, S.[Sidike], Reyes, A.A.[Abel A.], Kasaragod, A.[Anush], Oommen, T.[Thomas],
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification,
PBVS22(408-416)
IEEE DOI 2210
Training, Deep learning, Transforms, Logic gates, Pattern recognition, Object recognition, Task analysis BibRef

Assainova, O.[Olga], Rouot, J.[Jérémy], Sedgh-Gooya, E.[Ehsan],
Taming the curse of dimensionality for perturbed token identification,
IPTA20(1-6)
IEEE DOI 2206
Filtering, Scalability, Image processing, Tools, Nearest neighbor methods, Probabilistic logic, Tokenization, Nearest neighbor search BibRef

Wang, T.H.[Ting-Huai], Wang, G.M.[Guang-Ming], Tan, K.E.[Kuan Eeik], Tan, D.H.[Dong-Hui],
Hyperspectral Image Classification via Pyramid Graph Reasoning,
ISVC20(I:707-718).
Springer DOI 2103
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May, S.,
Recursive Hierarchical Clustering for Hyperspectral Images,
ISPRS20(B3:461-465).
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Santiago, J.G.[J. González], Schenkel, F., Gross, W., Middelmann, W.,
An Unsupervised Labeling Approach for Hyperspectral Image Classification,
ISPRS20(B3:407-415).
DOI Link 2012
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Hallé, P., Le Moan, S.[Steven], Cariou, C.,
Towards a completely blind classifier for hyperspectral images,
IVCNZ17(1-6)
IEEE DOI 1902
feature extraction, geophysical image processing, image classification, pattern clustering, overall accuracy BibRef

Le Moan, S.[Steven], Cariou, C.,
Parameter-Free Density Estimation for Hyperspectral Image Clustering,
IVCNZ18(1-6)
IEEE DOI 1902
Kernel, Bandwidth, Hyperspectral imaging, Probability density function, Estimation, Frequency estimation, Parameter-free BibRef

Rathore, P., Bezdek, J.C., Kumar, D., Rajasegarar, S., Palaniswami, M.,
Approximate Cluster Heat Maps of Large High-Dimensional Data,
ICPR18(195-200)
IEEE DOI 1812
Clustering algorithms, Visualization, Approximation algorithms, Partitioning algorithms, Big Data, Heating systems, Approximate cluster heat maps BibRef

Akbari, D.,
A New Spectral-spatial Framework for Classification of Hyperspectral Data,
GeoDisast17(7-10).
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Zhong, Z., Fan, B., Bai, J., Xiang, S., Pan, C.,
Structured binary feature extraction for hyperspectral imagery classification,
ICIP17(525-529)
IEEE DOI 1803
Binary codes, Dimensionality reduction, Feature extraction, Hyperspectral imaging, Synchronous digital hierarchy, Training, structured regularization BibRef

Bo, C.J.[Chun-Juan], Wang, D.[Dong], Lu, H.C.[Hu-Chuan],
Hyperspectral Image Classification via a Joint Weighted K-Nearest Neighbour Approach,
HISP16(I: 349-360).
Springer DOI 1704
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Shen, Y.[Yu], Xiao, L.[Liang], Molaei, M.[Mohsen],
Joint Multiview Fused ELM Learning with Propagation Filter for Hyperspectral Image Classification,
HISP16(I: 374-388).
Springer DOI 1704
BibRef

Petersson, H., Gustafsson, D., Bergstrom, D.,
Hyperspectral image analysis using deep learning: A review,
IPTA16(1-6)
IEEE DOI 1703
feature extraction BibRef

Becek, K., Borkowski, A., Mekik, Ç.,
A Study Of The Impact Of Insolation On Remote Sensing-based Landcover And Landuse Data Extraction,
ISPRS16(B7: 65-69).
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normalized total insolation index (NTII) from lidar. Estimate pixel reflectance dependency. BibRef

Movia, A., Beinat, A., Sandri, T.,
Land Use Classification from VHR Aerial Images Using Invariant Colour Components And Texture,
ISPRS16(B7: 311-317).
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Gewali, U.B., Monteiro, S.T.,
A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes,
ICIP16(2216-2220)
IEEE DOI 1610
Gaussian processes BibRef

Xu, Y., Wu, Z., Wei, Z., Dalla Mura, M., Chanussot, J., Bertozzi, A.,
GAS plume detection in hyperspectral video sequence using low rank representation,
ICIP16(2221-2225)
IEEE DOI 1610
Decision support systems BibRef

Shurygin, B., Shestakova, M., Nikolenko, A., Badasen, E., Strakhov, P.,
Accounting For Variance In Hyperspectral Data Coming From Limitations Of The Imaging System,
ISPRS16(B7: 365-369).
DOI Link 1610
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Savorskiy, V., Loupian, E., Balashov, I., Kashnitskii, A., Konstantinova, A., Tolpin, V., Uvarov, I., Kuznetsov, O., Maklakov, S., Panova, O., Savchenko, E.,
Vega-constellation Tools To Analize Hyperspectral Images,
ISPRS16(B4: 235-242).
DOI Link 1610
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Kurz, T.H., Buckley, S.J.,
A Review Of Hyperspectral Imaging In Close Range Applications,
ISPRS16(B5: 865-870).
DOI Link 1610
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Honkavaara, E., Hakala, T., Nevalainen, O., Viljanen, N., Rosnell, T., Khoramshahi, E., Näsi, R., Oliveira, R., Tommaselli, A.,
Geometric And Reflectance Signature Characterization Of Complex Canopies Using Hyperspectral Stereoscopic Images From UAV And Terrestrial Platforms,
ISPRS16(B7: 77-82).
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Walczykowski, P., Jenerowicz, A., Orych, A., Siok, K.,
Determining Spectral Reflectance Coefficients From Hyperspectral Images Obtained From Low Altitudes,
ISPRS16(B7: 107-110).
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Hoang, N.T.[Nguyen Tien], Koike, K.[Katsuaki],
Hyperspectral Transformation From Eo-1 Ali Imagery Using Pseudo-hyperspectral Image Synthesis Algorithm,
ISPRS16(B7: 661-665).
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Zhang, Y., Huynh, C.P., Habili, N., Ngan, K.N.,
Material segmentation in hyperspectral images with minimal region perimeters,
ICIP16(834-838)
IEEE DOI 1610
Hyperspectral imaging BibRef

Walczykowski, P., Siok, K., Jenerowicz, A.,
Methodology For Determining Optimal Exposure Parameters Of A Hyperspectral Scanning Sensor,
ISPRS16(B1: 1065-1069).
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Santos, A.B.[Andrey Bicalho], de Albuquerque Araujo, A.[Arnaldo], Schwartz, W.R.[William Robson], Menotti, D.[David],
Hyperspectral image interpretation based on partial least squares,
ICIP15(1885-1889)
IEEE DOI 1512
Extended morphological profile BibRef

Liu, Y.Z.[Ya-Zhou], Cao, G.[Guo], Sun, Q.S.[Quan-Sen], Siegel, M.[Mel],
Hyperspectral classification via learnt features,
ICIP15(2591-2595)
IEEE DOI 1512
Deep learning BibRef

Ni, D.[Ding], Ma, H.B.[Hong-Bing],
A sample set perspective on the classification of hyperspectral image with weighted affine constraint,
ICIP15(581-585)
IEEE DOI 1512
Hyperspectral image. Add neighbors to the sample set. BibRef

Jia, S.[Sen], Zhang, X.J.[Xiu-Jun], Deng, L.[Lin], Shu, Z.Q.[Zhen-Qiu],
An L_1/2 regularized low-rank representation for hyperspectral imagery classification,
ICIP15(1777-1780)
IEEE DOI 1512
Hyperspectral imagery classification; low-rank representation BibRef

Pervez, W., Khan, S.A., Valiuddin,
Hyperspectral Hyperion Imagery Analysis and Its Application Using Spectral Analysis,
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Image classification in natural scenes: Are a few selective spectral channels sufficient?,
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Accuracy. Does hyperspectral really add anything? Maybe not. BibRef

Fowler, J.E.[James E.],
Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging,
ICIP14(684-688)
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Hyperspectral imaging BibRef

Li, H.C.[Hai-Chang], Duan, J.Y.[Jiang-Yong], Xiang, S.M.[Shi-Ming], Wang, L.F.[Ling-Feng], Pan, C.H.[Chun-Hong],
Local Label Probability Propagation for Hyperspectral Image Classification,
ICPR14(4251-4256)
IEEE DOI 1412
Accuracy BibRef

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A classwise supervised ordering approach for morphology based hyperspectral image classification,
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Classification of high-dimensional data using the Sparse Matrix Transform,
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Tensor-Driven Hyperspectral Denoising: A Strong Link for Classification Chains?,
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Sensitivity analysis of support vector machine in classification of hyperspectral imagery,
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Evaluation Of A Lightweigth Uas-prototype For Hyperspectral Imaging.,
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Kernel methods in orthogonalization of multi-and hypervariate data,
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Li, J.M.[Ji-Ming], Hu, Z.F.[Zhen-Fang], Qian, Y.T.[Yun-Tao],
Hyperspectral data classification using Margin Infused Relaxed Algorithm,
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Li, J.M.[Ji-Ming], Qian, Y.T.[Yun-Tao], Jia, S.[Sen],
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Li, J.M.[Ji-Ming], Qian, Y.T.[Yun-Tao],
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Segmentation approach and comparison to hyperspectral object detection algorithms,
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Gupta, N.[Neelam],
Development of spectropolarimetric imagers for imaging of desert soils,
AIPR14(1-7)
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Development of staring hyperspectral imagers,
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Fused spectropolarimetric visible near-IR imaging,
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IEEE DOI 0310
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Hinnrichs, M., Gupta, N., Goldberg, A.,
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LDA/SVM Driven Nearest Neighbor Classification,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data, Neural Networks for Classification .

Last update:Sep 28, 2024 at 17:47:54