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Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation.

T. Guo, F. Luo, L. Zhang, X. Tan, J. Liu, and X. Zhou. IEEE Geosci. Remote. Sens. Lett., 17 (4): 716-720 (2020)

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Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning.H. Huang, G. Shi, H. He, Y. Duan, and F. Luo. CoRR, (2018)Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation.T. Guo, F. Luo, L. Zhang, X. Tan, J. Liu, and X. Zhou. IEEE Geosci. Remote. Sens. Lett., 17 (4): 716-720 (2020)Adaptive Weighted Total Variation Minimization Based Alternating Direction Method of Multipliers for Limited Angle CT Reconstruction.F. Luo, W. Li, W. Tu, and W. Wu. IEEE Access, (2018)Discriminant Spatial-Spectral Hypergraph Learning for Hyperspectral Image Classification.F. Luo, L. Zhang, B. Du, L. Zhang, and Y. Dong. IGARSS, page 8480-8483. IEEE, (2018)Hyperspectral Image Classification using Local spectral angle-Based Manifold Learning.F. Luo, J. Liu, H. Huang, and Y. Liu. IJPRAI, (2014)Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery.F. Luo, H. Huang, Y. Duan, J. Liu, and Y. Liao. Remote Sensing, 9 (8): 790 (2017)Dimensionality Reduction and Classification of Hyperspectral Image via Multistructure Unified Discriminative Embedding.F. Luo, Z. Zou, J. Liu, and Z. Lin. IEEE Trans. Geosci. Remote. Sens., (2022)Semisupervised Sparse Manifold Discriminative Analysis for Feature Extraction of Hyperspectral Images.F. Luo, H. Huang, Z. Ma, and J. Liu. IEEE Trans. Geoscience and Remote Sensing, 54 (10): 6197-6211 (2016)Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image.F. Luo, B. Du, L. Zhang, L. Zhang, and D. Tao. IEEE Trans. Cybernetics, 49 (7): 2406-2419 (2019)DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT.W. Wu, H. Yu, P. Chen, F. Luo, F. Liu, Q. Wang, Y. Zhu, Y. Zhang, J. Feng, and H. Yu. CoRR, (2019)

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Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation.

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Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation.