Welcome to the home page of Lina Zhuang (庄丽娜)

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 the IEEE Transactions on Geoscience and Remote Sensing (TGRS)

• Associate Editor of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)

• Associate Editor of the 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*)

• Eigen-CNN: Eigenimages plus Eigennoise Level Maps Guided Network for Hyperspectral Image Denoising [PDF, Matlab Code]

• Sliding Dual-Window-Inspired Reconstruction Network for Hyperspectral Anomaly Detection [PDF, Matlab Code]

• BockNet: Blind-block reconstruction network with a guard window for hyperspectral anomaly detection [PDF, Matlab Code]

• 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, Matlab Code]

• PDBSNet: Pixel-shuffle Down-sampling Blind-Spot Reconstruction Network for Hyperspectral Anomaly Detection [PDF, Matlab Code]

• 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, Matlab Code] 

• 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

• Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations, [PDF, Matlab Code] 

 ESI highly cited paper

• Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations, [PDF, Matlab Code]

ESI highly cited paper

•  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

• 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:

• 2024, IEEE TGRS Best Reviewers Award

• 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;