14.2.5.1 Semi-Supervised Clustering Applied to Hyperspectral Data

Chapter Contents (Back)
Semi-Supervised. Semi-Supervised Learning. Hyperspectral. Semi-Supervised Clustering.

Dópido, I.[Inmaculada], Li, J.[Jun], Marpu, P.R.[Prashanth Reddy], Plaza, A.[Antonio], Dias, J.M.B.[José M. Bioucas], Benediktsson, J.A.[Jon Atli],
Semisupervised Self-Learning for Hyperspectral Image Classification,
GeoRS(51), No. 7, 2013, pp. 4032-4044.
IEEE DOI Support vector machines; semisupervised self-learning 1307
BibRef

Guo, X., Huang, X., Zhang, L., Zhang, L., Plaza, A., Benediktsson, J.A.,
Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery,
GeoRS(54), No. 6, June 2016, pp. 3248-3264.
IEEE DOI 1606
geophysical image processing BibRef

Tan, K.[Kun], Li, E.[Erzhu], Du, Q.[Qian], Du, P.J.[Pei-Jun],
An efficient semi-supervised classification approach for hyperspectral imagery,
PandRS(97), No. 1, 2014, pp. 36-45.
Elsevier DOI 1410
Hyperspectral BibRef

Tan, K.[Kun], Hu, J.[Jun], Li, J.[Jun], Du, P.J.[Pei-Jun],
A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination,
PandRS(105), No. 1, 2015, pp. 19-29.
Elsevier DOI 1506
Semi-supervised classification BibRef

Tan, K.[Kun], Zhu, J.S.[Ji-Shuai], Du, Q.[Qian], Wu, L.X.[Li-Xin], Du, P.J.[Pei-Jun],
A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement,
RS(8), No. 9, 2016, pp. 749.
DOI Link 1610
BibRef

Ou, D.[Depin], Tan, K.[Kun], Du, Q.[Qian], Zhu, J.S.[Ji-Shuai], Wang, X.[Xue], Chen, Y.[Yu],
A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

He, Z.[Zhi], Liu, L.[Lin], Zhou, S.H.[Su-Hong], Shen, Y.[Yi],
Learning Group-Based Sparse and Low-Rank Representation for Hyperspectral Image Classification,
PR(60), No. 1, 2016, pp. 1041-1056.
Elsevier DOI 1609
Classification BibRef

He, Z.[Zhi], Li, J.[Jun], Liu, L.[Lin],
Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification,
RS(8), No. 8, 2016, pp. 636.
DOI Link 1609
BibRef

Lu, Z.W.[Zhi-Wu], Wang, L.W.[Li-Wei],
Noise-robust semi-supervised learning via fast sparse coding,
PR(48), No. 2, 2015, pp. 605-612.
Elsevier DOI 1411
Graph-based semi-supervised learning BibRef

Romaszewski, M.[Michal], Glomb, P.[Przemyslaw], Cholewa, M.[Michal],
Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach,
PandRS(121), No. 1, 2016, pp. 60-76.
Elsevier DOI 1609
Hyperspectral classification BibRef

Xu, L., Clausi, D.A., Li, F., Wong, A.,
Weakly Supervised Classification of Remotely Sensed Imagery Using Label Constraint and Edge Penalty,
GeoRS(55), No. 3, March 2017, pp. 1424-1436.
IEEE DOI 1703
Correlation BibRef

Li, F., Clausi, D.A., Xu, L., Wong, A.,
ST-IRGS: A Region-Based Self-Training Algorithm Applied to Hyperspectral Image Classification and Segmentation,
GeoRS(56), No. 1, January 2018, pp. 3-16.
IEEE DOI 1801
Gaussian distribution, hyperspectral imaging, image classification, image segmentation, scene classification BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Su, H.J.[Hong-Jun], Zhou, S.G.[Shao-Guang],
Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Ma, L., Crawford, M.M., Tian, J.,
Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification,
GeoRS(48), No. 11, November 2010, pp. 4099-4109.
IEEE DOI 1011
BibRef

Ma, L.[Li], Crawford, M.M., Yang, X.Q.[Xiao-Quan], Guo, Y.[Yan],
Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification,
GeoRS(53), No. 5, May 2015, pp. 2832-2844.
IEEE DOI 1502
computational geometry BibRef

Ma, L.[Li], Ma, A.D.[An-Dong], Ju, C.[Cai], Li, X.M.[Xing-Mei],
Graph-Based Semi-Supervised Learning for Spectral-Spatial Hyperspectral Image Classification,
PRL(83, Part 2), No. 1, 2016, pp. 133-142.
Elsevier DOI 1609
Hyperspectral images BibRef

Shao, Y.J.[Yuan-Jie], Sang, N.[Nong], Gao, C.X.[Chang-Xin], Ma, L.[Li],
Probabilistic class structure regularized sparse representation graph for semi-supervised hyperspectral image classification,
PR(63), No. 1, 2017, pp. 102-114.
Elsevier DOI 1612
Graph BibRef

Shao, Y.J.[Yuan-Jie], Sang, N.[Nong], Gao, C.X.[Chang-Xin],
Representation Space-Based Discriminative Graph Construction for Semisupervised Hyperspectral Image Classification,
SPLetters(25), No. 1, January 2018, pp. 35-39.
IEEE DOI 1801
geophysical image processing, graph theory, hyperspectral imaging, image classification, semisupervised learning (SSL) BibRef

Shao, Y.J.[Yuan-Jie], Sang, N.[Nong], Gao, C.X.[Chang-Xin], Ma, L.[Li],
Spatial and Class Structure Regularized Sparse Representation Graph for Semi-Supervised Hyperspectral Image Classification,
PR(81), 2018, pp. 81-94.
Elsevier DOI 1806
Spatial regularization, Probabilistic class structure, Sparse representation (SR), Semi-supervised learning (SSL), Hyperspectral image (HSI) classification BibRef

Kong, Y.[Yi], Wang, X.S.[Xue-Song], Cheng, Y.[Yuhu], Chen, C.L.P.[C. L. Philip],
Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhao, G.X.[Gui-Xin], Wang, X.S.[Xue-Song], Kong, Y.[Yi], Cheng, Y.[Yuhu],
Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Hu, X.P.[Xiao-Pei], Zhao, G.X.[Gui-Xin], Dong, A.[Aimei], Lv, G.H.[Guo-Hua], Zhai, Y.[Yi], Guo, Y.[Ying], Dong, X.J.[Xiang-Jun],
Few-Shot Hyperspectral Image Classification with Spectral-Spatial Feature Fusion Based on Fuzzy Broad Learning System,
ICIP23(3160-3164)
IEEE DOI 2312
BibRef

Guo, Y.[Ying], He, M.Y.[Ming-Yi], Fan, B.[Bin],
Grid-Transformer for Few-Shot Hyperspectral Image Classification,
ICIP23(755-759)
IEEE DOI 2312
BibRef

Cao, M.X.[Meng-Xin], Zhao, G.X.[Gui-Xin], Dong, A.[Aimei], Lv, G.H.[Guo-Hua], Guo, Y.[Ying], Dong, X.J.[Xiang-Jun],
Few-Shot Hyperspectral Image Classification Based on Cross-Domain Spectral Semantic Relation Transformer,
ICIP23(1375-1379)
IEEE DOI 2312
BibRef

Xie, F.D.[Fu-Ding], Hu, D.C.[Dong-Cui], Li, F.F.[Fang-Fei], Yang, J.[Jun], Liu, D.S.[De-Shan],
Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Wu, Y.[Yue], Mu, G.F.[Gui-Feng], Qin, C.[Can], Miao, Q.G.[Qi-Guang], Ma, W.P.[Wen-Ping], Zhang, X.R.[Xiang-Rong],
Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

He, F.[Fang], Wang, R.[Rong], Jia, W.M.[Wei-Min],
Fast semi-supervised learning with anchor graph for large hyperspectral images,
PRL(130), 2020, pp. 319-326.
Elsevier DOI 2002
Hyperspectral images (HSI) classification, Graph-based semi-supervised learning (SSL), Anchor graph BibRef

Shaik, R.U.[Riyaaz Uddien], Unni, A.[Aiswarya], Zeng, W.P.[Wei-Ping],
Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef


Ling, Z.G.[Zhi-Gang], Li, X.X.[Xiu-Xin], Zou, W.[Wen], Guo, S.[Siyu],
Semi-Supervised Learning via Convolutional Neural Network for Hyperspectral Image Classification,
ICPR18(1-6)
IEEE DOI 1812
Feature extraction, Training, Support vector machines, Convolution, Hyperspectral imaging, Semisupervised learning, hyperspectral image classification BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Iterative, Hierarchical Clustering Techniques .


Last update:Apr 18, 2024 at 11:38:49