Review on graph learning for dimensionality reduction of hyperspectral image

References

• Amini, S., S. Homayouni, A. Safari, and A. Darvishsefat. 2018. “Object-based Classification of Hyperspectral Data Using Random Forest Algorithm.” Geo-spatial Information Science 21 (2): 127–138. doi:10.1080/10095020.2017.1399674.
   Web of Science ®Google Scholar
• An, J. L., J. H. Lei, Y. Z. Song, X. R. Zhang, and J. M. Guo. 2019. “Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction.” Remote Sensing 11 (12): 1485. doi:10.3390/rs11121485.
   Web of Science ®Google Scholar
• Belkin, M., and P. Niyogi. 2003. “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.” Neural Computation 15 (6): 1373–1396. doi:10.1162/089976603321780317.
   Web of Science ®Google Scholar
• Cao, F. X., Z. J. Yang, J. C. Ren, W. Z. Chen, G. J. Han, and Y. Z. Shen. 2019. “Local Block Multilayer Sparse Extreme Learning Machine for Effective Feature Extraction and Classification of Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 5580–5594. doi:10.1109/tgrs.2019.2900509.
   Web of Science ®Google Scholar
• Chen, F., D. Xiao, and Z. Li. 2016. “Developing Water Quality Retrieval Models with in Situ Hyperspectral Data in Poyang Lake, China.” Geo-spatial Information Science 19 (4): 255–266. doi:10.1080/10095020.2016.1258201.
   Web of Science ®Google Scholar
• Chen, Y., K. Zhu, L. Zhu, X. He, P. Ghamisi, and J. A. Benediktsson. 2019. “Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (9): 7048–7066. doi:10.1109/TGRS.2019.2910603.
   Web of Science ®Google Scholar
• Cheng, B., J. C. Yang, S. C. Yan, Y. Fu, and T. S. Huang. 2010. “Learning with l(1)-Graph for Image Analysis.” IEEE Transactions on Image Processing 19 (4): 858–866. doi:10.1109/tip.2009.2038764.
   PubMed Web of Science ®Google Scholar
• Datta, A., S. Ghosh, and A. Ghosh. 2017. “Supervised Feature Extraction of Hyperspectral Images Using Partitioned Maximum Margin Criterion.” IEEE Geoscience and Remote Sensing Letters 14 (1): 82–86. doi:10.1109/lgrs.2016.2628078.
   Web of Science ®Google Scholar
• Deng, Y. J., H. C. Li, K. Fu, Q. Du, and W. J. Emery. 2018. “Tensor Low-Rank Discriminant Embedding for Hyperspectral Image Dimensionality Reduction.” IEEE Transactions on Geoscience and Remote Sensing 56 (12): 7183–7194. doi:10.1109/tgrs.2018.2849085.
   Web of Science ®Google Scholar
• Du, W. B., W. W. Qiang, M. Lv, Q. L. Hou, L. Zhen, and L. Jing. 2018. “Semi-supervised Dimension Reduction Based on Hypergraph Embedding for Hyperspectral Images.” International Journal of Remote Sensing 39 (6): 1696–1712. doi:10.1080/01431161.2017.1415480.
   Web of Science ®Google Scholar
• Frazier, A., L. Wang, and J. Chen. 2014. “Two New Hyperspectral Indices for Comparing Vegetation Chlorophyll Content.” Geo-spatial Information Science 17 (1): 17–25. doi:10.1080/10095020.2014.889264.
   Google Scholar
• Hang, R. L., and Q. S. Liu. 2018. “Dimensionality Reduction of Hyperspectral Image Using Spatial Regularized Local Graph Discriminant Embedding.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (9): 3262–3271. doi:10.1109/jstars.2018.2847042.
   Web of Science ®Google Scholar
• Huang, H., and Y. B. Huang. 2014. “Improved Discriminant Sparsity Neighborhood Preserving Embedding for Hyperspectral Image Classification.” Neurocomputing 136: 224–234. doi:10.1016/j.neucom.2014.01.010.
   Web of Science ®Google Scholar
• Huang, H., and M. Yang. 2015. “Dimensionality Reduction of Hyperspectral Images with Sparse Discriminant Embedding.” IEEE Transactions on Geoscience and Remote Sensing 53 (9): 5160–5169. doi:10.1109/TGRS.2015.2418203.
   Web of Science ®Google Scholar
• Huang, S., D. Yang, Y. X. Ge, and X. H. Zhang. 2016. “Discriminant Hyper-Laplacian Projections and Its Scalable Extension for Dimensionality Reduction.” Neurocomputing 173: 145–153. doi:10.1016/j.neucom.2015.01.101.
   Web of Science ®Google Scholar
• Huang, X., J. Li, W. Liao, and J. Chanussot. 2017. “Information Extraction from Remote Sensing Imagery.” Geo-spatial Information Science 20 (4): 297–298. doi:10.1080/10095020.2017.1404273.
   Web of Science ®Google Scholar
• Huang, Z. Y., H. Y. Zhu, J. T. Zhou, and X. Peng. 2019. “Multiple Marginal Fisher Analysis.” IEEE Transactions on Industrial Electronics 66 (12): 9798–9807. doi:10.1109/tie.2018.2870413.
   Web of Science ®Google Scholar
• Ji, R. R., Y. Gao, R. C. Hong, Q. Liu, D. C. Tao, and X. L. Li. 2014. “Spectral-Spatial Constraint Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 52 (3): 1811–1824. doi:10.1109/Tgrs.2013.2255297.
   Web of Science ®Google Scholar
• Kuo, B. C., C. H. Li, and J. M. Yang. 2009. “Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 47 (4): 1139–1155. doi:10.1109/tgrs.2008.2008308.
   Web of Science ®Google Scholar
• Li, D. Q., X. S. Wang, and Y. H. Cheng. 2019. “Spatial-spectral Neighbour Graph for Dimensionality Reduction of Hyperspectral Image Classification.” International Journal of Remote Sensing 40 (11): 4361–4383. doi:10.1080/01431161.2018.1562587.
   Web of Science ®Google Scholar
• Li, S. T., W. W. Song, L. Y. Fang, Y. S. Chen, P. Ghamisi, and J. A. Benediktsson. 2019. “Deep Learning for Hyperspectral Image Classification: An Overview.” IEEE Transactions on Geoscience and Remote Sensing 57 (9): 6690–6709. doi:10.1109/tgrs.2019.2907932.
   Web of Science ®Google Scholar
• Licciardi, G., and J. Chanussot. 2018. “Spectral Transformation Based on Nonlinear Principal Component Analysis for Dimensionality Reduction of Hyperspectral Images.” European Journal of Remote Sensing 51 (1): 375–390. doi:10.1080/22797254.2018.1441670.
   Web of Science ®Google Scholar
• Liu, Y., A. Singleton, and D. Arribas-Bel. 2019. “A Principal Component Analysis (Pca)-based Framework for Automated Variable Selection in Geodemographic Classification.” Geo-spatial Information Science 22 (4): 251–264. doi:10.1080/10095020.2019.1621549.
   Web of Science ®Google Scholar
• Liu, Z., B. Tang, X. He, Q. Qiu, and F. Liu. 2017. “Class-Specific Random Forest with Cross-Correlation Constraints for Spectral-Spatial Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 14 (2): 257–261. doi:10.1109/Lgrs.2016.2637561.
   Web of Science ®Google Scholar
• Lu, G., Z. Jin, and J. Zou. 2012. “Face Recognition Using Discriminant Sparsity Neighborhood Preserving Embedding.” Knowledge-Based Systems 31: 119–127. doi:10.1016/j.knosys.2012.02.014.
   Web of Science ®Google Scholar
• Luo, F., H. Huang, Y. Duan, J. Liu, and Y. Liao. 2017a. “Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery.” Remote Sensing 9 (8): 790. doi:10.3390/rs9080790.
   Web of Science ®Google Scholar
• Luo, F. L., B. Du, L. P. Zhang, L. F. Zhang, and D. C. Tao. 2019. “Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image.” IEEE Transactions on Cybernetics 49 (7): 2406–2419. doi:10.1109/tcyb.2018.2810806.
   PubMed Web of Science ®Google Scholar
• Luo, F. L., H. Huang, J. M. Liu, and Z. Z. Ma. 2017b. “Fusion of Graph Embedding and Sparse Representation for Feature Extraction and Classification of Hyperspectral Imagery.” Photogrammetric Engineering and Remote Sensing 83 (1): 37–46. doi:10.14358/pers.83.1.37.
   Web of Science ®Google Scholar
• Ly, N., Q. Du, and J. Fowler. 2014. “Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 52 (7): 3872–3884. doi:10.1109/TGRS.2013.2277251.
   Web of Science ®Google Scholar
• Mohanty, R., S. L. Happy, and A. Routray. 2019a. “A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut with HGF for Dimensionality Reduction of Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 3423–3435. doi:10.1109/tgrs.2018.2884771.
   Web of Science ®Google Scholar
• Mohanty, R., S. L. Happy, and A. Routray. 2019b. “Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 16 (6): 932–936. doi:10.1109/lgrs.2018.2885809.
   Web of Science ®Google Scholar
• Natsagdorj, E., T. Renchin, M. Kappas, B. Tseveen, C. Dari, O. Tsend, and U. Duger. 2017. “An Integrated Methodology for Soil Moisture Analysis Using Multispectral Data in Mongolia.” Geo-spatial Information Science 20 (1): 46–55. doi:10.1080/10095020.2017.1307666.
   Web of Science ®Google Scholar
• Pan, T. T., E. Chyngyz, D. W. Sun, J. Paliwal, and H. B. Pu. 2019. “Pathogenetic Process Monitoring and Early Detection of Pear Black Spot Disease Caused by Alternaria Alternata Using Hyperspectral Imaging.” Postharvest Biology and Technology 154: 96–104. doi:10.1016/j.postharvbio.2019.04.005.
   Web of Science ®Google Scholar
• Peng, J., L. Li, and Y. Y. Tang. 2019. “Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images.” IEEE Transactions on Neural Networks and Learning Systems 30 (6): 1790–1802. doi:10.1109/TNNLS.2018.2874432.
   PubMed Web of Science ®Google Scholar
• Peng, J. T., and T. Luo. 2016. “Sparse Matrix Transform-based Linear Discriminant Analysis for Hyperspectral Image Classification.” Signal Image and Video Processing 10 (4): 761–768. doi:10.1007/s11760-015-0808-y.
   Web of Science ®Google Scholar
• Qiao, L., S. Chen, and X. Tan. 2010. “Sparsity Preserving Projections with Applications to Face Recognition.” Pattern Recognition 43 (1): 331–341. doi:10.1016/j.patcog.2009.05.005.
   Web of Science ®Google Scholar
• Roweis, S. T., and L. K. Saul. 2000. “Nonlinear Dimensionality Reduction by Locally Linear Embedding.” Science 290 (5500): 2323–2326. doi:10.1126/science.290.5500.2323.
   PubMed Web of Science ®Google Scholar
• Song, M. P., X. D. Shang, Y. L. Wang, C. Y. Yu, and C. I. Chang. 2019. “Class Information-Based Band Selection for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (11): 8394–8416. doi:10.1109/tgrs.2019.2920891.
   Web of Science ®Google Scholar
• Sun, Y. B., S. J. Wang, Q. S. Liu, R. L. Hang, and G. C. Liu. 2017. “Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images.” Remote Sensing 9 (5): 506. doi:10.3390/rs9050506.
   Web of Science ®Google Scholar
• Tenenbaum, J. B., V. de Silva, and J. C. Langford. 2000. “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” Science 290 (5500): 2319–2323. doi:10.1126/science.290.5500.2319.
   PubMed Web of Science ®Google Scholar
• Wang, Z., Q. Q. Ruan, and G. Y. An. 2016. “Facial Expression Recognition Using Sparse Local Fisher Discriminant Analysis.” Neurocomputing 174: 756–766. doi:10.1016/j.neucom.2015.09.083.
   Web of Science ®Google Scholar
• Wright, J., A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. 2009. “Robust Face Recognition via Sparse Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2): 210–227. doi:10.1109/TPAMI.2008.79.
   PubMed Web of Science ®Google Scholar
• Wu, Y. F., L. R. Gao, B. Zhang, H. N. Zhao, and J. Li. 2014. “Real-time Implementation of Optimized Maximum Noise Fraction Transform for Feature Extraction of Hyperspectral Images.” Journal of Applied Remote Sensing 8. doi:10.1117/1.jrs.8.084797.
   Web of Science ®Google Scholar
• Yakovliev, A., R. Ziniuk, D. Wang, B. Xue, L. O. Vretik, O. A. Nikolaeva, M. Tan, et al. 2019. “Hyperspectral Multiplexed Biological Imaging of Nanoprobes Emitting in the Short-Wave Infrared Region.” Nanoscale Research Letters 14. doi:10.1186/s11671-019-3068-x.
   PubMed Web of Science ®Google Scholar
• Yan, S., D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin. 2007. “Graph Embedding and Extensions: A General Framework for Dimensionality Reduction.” IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (1): 40–51. doi:10.1109/TPAMI.2007.250598.
   PubMed Web of Science ®Google Scholar
• Yuan, H. L., and Y. Y. Tang. 2015. “Learning with Hypergraph for Hyperspectral Image Feature Extraction.” IEEE Geoscience and Remote Sensing Letters 12 (8): 1695–1699. doi:10.1109/Lgrs.2015.2419713.
   Web of Science ®Google Scholar
• Zang, F., and J. Zhang. 2011. “Discriminative Learning by Sparse Representation for Classification.” Neurocomputing 74: 2176–2183. doi:10.1016/j.neucom.2011.02.012.
   Web of Science ®Google Scholar
• Zhai, Y. G., L. F. Zhang, N. Wang, Y. Guo, Y. Cen, T. X. Wu, and Q. X. Tong. 2016. “A Modified Locality-Preserving Projection Approach for Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 13 (8): 1059–1063. doi:10.1109/lgrs.2016.2564993.
   Web of Science ®Google Scholar
• Zhang, C. J., G. D. Li, and S. H. Du. 2019. “Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (11): 9201–9222. doi:10.1109/tgrs.2019.2925615.
   Web of Science ®Google Scholar
• Zhang, L., and B. Du. 2012. “Recent Advances in Hyperspectral Image Processing.” Geo-spatial Information Science 15 (3): 143–156. doi:10.1080/10095020.2012.719684.
   Google Scholar
• Zhao, B., L. Gao, W. Liao, and B. Zhang. 2017. “A New Kernel Method for Hyperspectral Image Feature Extraction.” Geo-spatial Information Science 20 (4): 309–318. doi:10.1080/10095020.2017.1403088.
   Web of Science ®Google Scholar
• Zhou, Y. C., J. T. Peng, and C. L. P. Chen. 2015. “Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 1082–1095. doi:10.1109/tgrs.2014.2333539.
   Web of Science ®Google Scholar