Welcome to the home page of Lina Zhuang (庄丽娜)
Address: No.9 Dengzhuang South Road,
Haidan District, Beijing 100094, China
Office: Building C, room 716
Email: linazhuang@qq.com,zhuangln@aircas.ac.cn
(Note that lzhuang@lx.it.pt, linazhuang@hkbu.edu.hk, and linaz@hku.hk are not used anymore)
Academic Degrees
Ph.D. in Electrical and Computer Engineering, Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal (2018)
Supervisor: Jose M. Bioucas-Dias (IEEE Fellow), Co-supervisor: Mario A. T. Figueiredo (IEEE Fellow)
M.Sc. in Cartography and geography information system, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China (2015)
Supervisor: Bing Zhang (IEEE Fellow), Co-supervisor: Lianru Gao
B.S. in Geography information system (Dual degree in Economics), South China Normal University, Guangzhou, China (2012)
Working experience:
2023.11 - present: Professor in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
2022.04 - 2023.10: Associate Professor in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
2021.03 - 2022.02: Research Assistant Professor in the Department of Mathematics, the University of Hong Kong
2019.03 - 2021.02: Research Assistant Professor in the Department of Mathematics, Hong Kong Baptist University
2015.09 - 2018.08: Marie Sklodowska-Curie early-stage researcher hosted by Instituto de Telecomunicações, Portugal.
Professional service
Associate Editor of IET Image Processing journal
Chairing and organization:
Session Organizer: 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · Brussels, Belgium
Session Chairs (TH2.MM-1 and TH2.MM-11): 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · Brussels, Belgium
Courses taught at HKBU
MATH 3615: Introduction to Imaging Science (2019-2020, S2)
GFQR 1037: Hands on Little and Big Data (2020-2021, S1)
Publications records: Google Scholar, ORCID
Journal articles:
(Corresponding author*)
Eigenimage2Eigenimage (E2E): A Self-Supervised Deep Learning Network for Hyperspectral Image Denoising [PDF]
A Self-supervised Deep Denoiser for Hyperspectral and Multispectral Image Fusion [PDF]
PDBSNet: Pixel-shuffle Down-sampling Blind-Spot Reconstruction Network for Hyperspectral Anomaly Detection [PDF]
Cross-track Illumination Correction for Hyperspectral Pushbroom Sensor Images using Low-rank and Sparse Representations [PDF]
Hyperspectral Anomaly Detection Based on Chessboard Topology [PDF]
BS3LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection [PDF]
Nonlocal Self-similarity-based Hyperspectral Remote Sensing Image Denoising With 3-D Convolutional Neural Network [PDF]
FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal, [PDF, Matlab Code]
Hy-demosaicing: Hyperspectral Blind Reconstruction From Spectral Subsampling,[PDF, Matlab Code]
Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior, [PDF, Matlab Code]
Adaptive Hhyperspectral Mixed Noise Removal,[PDF]
Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing, [PDF, Matlab Code]
ESI highly cited paper
Lianru Gao, Zhicheng Wang, Lina Zhuang*, Haoyang Yu, Bing Zhang, and Jocelyn Chanussot IEEE Transactions on Geoscience and Remote Sensing, 2021.Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations, [PDF, Matlab Code]
ESI highly cited paper
Lina Zhuang, Xiyou Fu, Michael K. Ng and José M. Bioucas-Dias, IEEE Transactions on Geoscience and Remote Sensing, 2020.Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations, [PDF, Matlab Code]
ESI highly cited paper
Lina Zhuang, Lianru Gao, Bing Zhang, Xiyou Fu and José M. Bioucas-Dias, IEEE Transactions on Geoscience and Remote Sensing, 2020.Hyperspectral Mixed Noise Removal By L1-Norm-Based Subspace Representation, [PDF, Matlab Code]
Regularization parameter selection in minimum volume hyperspectral unmixing, [PDF, Matlab Code]
Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification, [PDF]
Combining t-Distributed Stochastic Neighbor Embedding With Convolutional Neural Networks for Hyperspectral Image Classification, [PDF]
Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations, [PDF, Matlab Code]
ESI highly cited paper
Lina Zhuang and José M. Bioucas-Dias, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Mar. 2018.A new low-rank representation based hyperspectral image denoising method for mineral mapping, [PDF]
Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing,
A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm
Normal Endmember Spectral Unmixing Model for Hyperspectral Imagery, [PDF]
Multiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction from Hyperspectral Imagery,
PSO-EM: A Hyperspectral Unmixing Algorithm Based On Normal Compositional Model, [PDF]
Conference papers:
Hy-demosaicing: hyperspectral blind reconstruction from spectral subsampling, (Third place in student paper contest of IGARSS 2018) [PDF]
Adaptive hyperspectral mixed noise removal, [PDF]
Hyperspectral image denoising and anomaly detection based on low-rank and sparse representation, (Best student paper presentation award) [PDF, Matlab Code, Conference Presentation Video]
Hyperspectral image denoising based on global and non-local low-rank factorizations, [PDF, Matlab Code]
Class-adapted blind deblurring of document images, [PDF]
Hyperspectral image inpainting based on low-rank representation: a case study on tiangong-1 data, [PDF]
Fast Hyperspectral image denoising based on low-rank and sparse representations, [PDF]
Swarm Intelligence: A Reliable Solution for Extracting Endmembers from Hyperspectral Imagery, [PDF]
An Improved Expectation Maximization Algorithm for Hyperspectral Image Classification, [PDF]
Awards:
2023, Winner of National Science Fund for Excellent Young Scientists (Overseas)
2022, Winner of Hundred-Talent Programme of the Chinese Academy of Sciences
2018, Third place of student paper contest in IEEE International Geoscience and Remote Sensing Symposium 2018 (paper: Hy-domosaicing: hyperspectral blind reconstruction from spectral subsampling);
2017, Best student paper award at SPIE Remote Sensing and Security+Defence International Symposia 2017(paper: Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations);
2015-2018, Marie Skłodowska-Curie Fellowship programme;
2013 and 2011, National Scholarship;