When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
Xue, J, Zhao, Y, Bu, Y, Chan, JC-W & Kong, S 2022, 'When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion', IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13887-13901. https://doi.org/10.1109/TCYB.2021.3140148
An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight
Wang, H, Cheng, Y, Liu, N, zhao, Y, Chan, JC-W & Li, Z 2022, 'An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight', Remote Sensing, vol. 14, no. 16, 3869. https://doi.org/10.3390/rs14163869
Spectral Super-Resolution based on Dictionary Optimization Learning via Spectral Library
Yan, H-F, Zhao, Y-Q, Chan, JC-W & Kong, S 2022, 'Spectral Super-Resolution based on Dictionary Optimization Learning via Spectral Library', IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-16.
Histograms of oriented mosaic gradients for snapshot spectral image description
Chen, L, Zhao, Y, Chan, JC-W & Kong, S 2022, 'Histograms of oriented mosaic gradients for snapshot spectral image description', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 183, pp. 79-93. https://doi.org/10.1016/j.isprsjprs.2021.10.018
Illumination-invariant road detection and tracking using LWIR polarization characteristics
Li, N, Zhao, Y, Pan, Q, Kong, S & Chan, JC-W 2021, 'Illumination-invariant road detection and tracking using LWIR polarization characteristics', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 180, pp. 357-369. https://doi.org/10.1016/j.isprsjprs.2021.08.022
Variational regularization network with attentive deep prior for hyperspectral-multispectral image fusion
Yang, J, Xiao, L, Zhao, Y & Chan, JC-W 2021, 'Variational regularization network with attentive deep prior for hyperspectral-multispectral image fusion', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17.
Robust background features extraction through homogeneous region-based joint sparse representation for hyperspectral anomaly detection
Yang, Y, Song, S, Liu, D, Zhang, J & Chan, JC-W 2021, 'Robust background features extraction through homogeneous region-based joint sparse representation for hyperspectral anomaly detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 10, pp. 8723-8737.
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Xue, J, Zhao, Y, Huang, S, Liao, W, Chan, JC-W & Kong, S 2021, 'Multilayer sparsity-based tensor decomposition for low-rank tensor completion', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6916-6930.
SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification
Quan, Y, Zhong, X, Wei, FENG, Chan, JC-W, Li, Q & Xing, M 2021, 'SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification', Remote Sensing, vol. 13, no. 3, 464. https://doi.org/10.3390/rs13030464
Hybrid Local and Non-local 3D Attentive CNN for Hyperspectral Image Super-Resolution
Yang, J, Xiao, L, Zhao, Y & Chan, JC-W 2020, 'Hybrid Local and Non-local 3D Attentive CNN for Hyperspectral Image Super-Resolution', IEEE Geoscience and Remote Sensing Letters, pp. 1-5.
Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints
Yi, C, Zhao, Y, Chan, JC-W & Kong, SG 2020, 'Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints', Remote Sensing, vol. 12, no. 6, 993. https://doi.org/10.3390/rs12060993
Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples
Fang, B, Li, Y, Zhang, H & Chan, JC-W 2020, 'Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 161, pp. 164-178. https://doi.org/10.1016/j.isprsjprs.2020.01.015
Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping
Kong, X, Zhao, Y, Xue, J, Chan, JC-W & Kong, SG 2020, 'Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping', Remote Sensing, vol. 12, no. 4, 704. https://doi.org/10.3390/rs12040704
Enhanced Sparsity Prior Model for Low-rank Tensor Completion
Xue, J, Zhao, Y, Liao, W, Chan, JC-W & Kong, SG 2019, 'Enhanced Sparsity Prior Model for Low-rank Tensor Completion', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15. https://doi.org/10.1109/TNNLS.2019.2956153
Spectral super-resolution for multispectral image based on spectral improvement strategy and spatial preservation strategy
Yi, C, Zhao, Y & Chan, JC-W 2019, 'Spectral super-resolution for multispectral image based on spectral improvement strategy and spatial preservation strategy', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 9010-9024. https://doi.org/10.1109/TGRS.2019.2924096
Thin Cloud Removal with Residual Symmetrical Concatenation Network
Wenbo, L, Li, Y, Chan, JC-W & Chen, D 2019, 'Thin Cloud Removal with Residual Symmetrical Concatenation Network', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 153, pp. 137-150. https://doi.org/10.1016/j.isprsjprs.2019.05.003
Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches
Guanter, L, Brell, M, Chan, JC-W, Giardino, C, Gomez-Dans, J, Mielke, C, Morsdorf, F, Segl, K & Yokoya, N 2019, 'Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches', Surveys in Geophysics, vol. 40, no. 3, pp. 657-687. https://doi.org/10.1007/s10712-018-9485-z
Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening
Wang, D, Li, Y & Chan, JC-W 2019, 'Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening', Remote Sensing, vol. 11, no. 22.
Deformable Dictionary Learning for SAR Image Change Detection
Li, L, Zhao, Y, Sun, J, Stolkin, R, Pan, Q, Chan, JC-W, Kong, SG & Liu, Z 2018, 'Deformable Dictionary Learning for SAR Image Change Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, 8360150, pp. 4605-4617. https://doi.org/10.1109/TGRS.2018.2829630
Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion
Yi, C, Zhao, Y & Chan, JC-W 2018, 'Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion', IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 4165-4177. https://doi.org/10.1109/TGRS.2018.2828042
Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network
Yang, J, Zhao, Y & Chan, JC-W 2018, 'Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network', Remote Sensing, vol. 10, no. 5, 800. https://doi.org/10.3390/rs10050800
Coarse-to-fine salient object detection based on deep convolutional neural networks
Li, Y, Cui, F, Xue, X & Chan, JC-W 2018, 'Coarse-to-fine salient object detection based on deep convolutional neural networks', Signal Processing: Image Communication, vol. 64, pp. 21-32. https://doi.org/10.1016/j.image.2018.01.012
Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification
Yang, J, Zhao, Y & Chan, JC-W 2017, 'Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 8, pp. 4729-4742. https://doi.org/10.1109/TGRS.2017.2698503
Joint Hyperspectral Super-Resolution and Unmixing with Interactive Feedback
Yi, C, Zhao, Y, Yang, J, Chan, JC-W & Kong, SG 2017, 'Joint Hyperspectral Super-Resolution and Unmixing with Interactive Feedback', IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, 7893730, pp. 3823-3834. https://doi.org/10.1109/TGRS.2017.2681721
Polarization Guided Auto-Regressive Model for Depth Recovery
Reda, M, Zhao, Y & Chan, JC-W 2017, 'Polarization Guided Auto-Regressive Model for Depth Recovery', IEEE Photonics Journal, vol. 9, no. 3, pp. 1-16. https://doi.org/10.1109/JPHOT.2017.2706748
Coupled Sparse Denoising and Unmixing With Low-Rank Constraint for Hyperspectral Image
Yang, J, Zhao, Y, Chan, JC-W & Kong, SG 2016, 'Coupled Sparse Denoising and Unmixing With Low-Rank Constraint for Hyperspectral Image', IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 3, pp. 1818-1833. https://doi.org/10.1109/TGRS.2015.2489218
Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty
Ampe, E, Vanhamel, I, Salvadore, E, Dams, J, Bashir, I, Demarchi, L, Chan, JC-W, Sahli, H, Canters, F & Batelaan, O 2012, 'Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 6, pp. 1859-1867. <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6264066&conte>
An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery
Chan, JC-W, Beckers, P, Spanhove, T & Vanden Borre, J 2012, 'An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery', International Journal of Applied Earth Observation and Geoinformation, vol. 18, pp. 13-22.
Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments
Demarchi, L, Canters, F, Chan, JC-W & Van De Voorde, T 2012, 'Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments', IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, pp. 3409-3424.
Application of Machine Learning Techniques for Ecotope Classification based on Hyperspectral Images
Chan, JC-W & Paelinckx, D 2005, Application of Machine Learning Techniques for Ecotope Classification based on Hyperspectral Images. Final Report for the ECOMALT Project (SR/03/046), Unknown.
Content-Guided Convolutional Neural Network for Hyperspectral Image Classification
Liu, Q, Xiao, L, Yang, J & Chan, JC-W 2020, 'Content-Guided Convolutional Neural Network for Hyperspectral Image Classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 9, 9034507, pp. 6124-6137. https://doi.org/10.1109/TGRS.2020.2974134