Ghent University Academic Bibliography

Your filters: cql: author="Chanussot, Jocelyn" or (type any "bookEditor issueEditor" and editor="Chanussot, Jocelyn")

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Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution

Ke Zheng, Lianru Gao, Wenzhi Liao (UGent) , Danfeng Hong, Bing Zhang, Ximin Cui and Jocelyn Chanussot

(2021) IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 59(3). p.2487-2502
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- Miscellaneous
- open access
Foreword to the special Issue on Hyperspectral remote sensing and imaging spectroscopy

Saurabh Prasad, Wenzhi Liao (UGent) , Mingyi He and Jocelyn Chanussot

(2018) IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 11(4). p.1019-1021
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- Journal Article
- A1
- open access
Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping

Wenzhi Liao (UGent) , Frieke Vancoillie (UGent) , Lianru Gao, Liwei Li, Bing Zhang and Jocelyn Chanussot

(2018) IEEE ACCESS. 6. p.68716-68729
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- Book Chapter
Remote sensing data fusion : guided filter-based hyperspectral pansharpening and graph-based feature-level fusion

Wenzhi Liao (UGent) , Jocelyn Chanussot and Wilfried Philips (UGent)

(2017) Mathematical models for remote sensing image processing. p.243-275
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- Miscellaneous
- open access
Information extraction from remote sensing imagery

Xin Huang, Jiayi Li, Wenzhi Liao (UGent) and Jocelyn Chanussot

(2017) GEO-SPATIAL INFORMATION SCIENCE. 20(4). p.297-298
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Taking optimal advantage of fine spatial information : promoting partial image reconstruction for the morphological analysis of very-high-resolution images

Wenzhi Liao (UGent) , Jocelyn Chanussot, Mauro Dalla Mura, Xin Huang, Rik Bellens (UGent) , Sidharta Gautama (UGent) and Wilfried Philips (UGent)

(2017) IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. 5(2). p.8-28
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Fusion of spectral and spatial information for classification of hyperspectral remote-sensed imagery by local graph

Wenzhi Liao (UGent) , Mauro Dalla Mura, Jocelyn Chanussot and Aleksandra Pizurica (UGent)

(2016) IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 9(2). p.583-594
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Morphological attribute profiles with partial reconstruction

Wenzhi Liao (UGent) , Mauro Dalla Mura, Jocelyn Chanussot, Rik Bellens (UGent) and Wilfried Philips (UGent)

(2016) IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 54(3). p.1738-1756
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Lidar information extraction by attribute filters with partial reconstruction

Wenzhi Liao (UGent) , Mauro Dalla Mura, Xin Huang, Jocelyn Chanussot, Sidharta Gautama (UGent) , Paul Scheunders and Wilfried Philips (UGent)

(2016) 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). In IEEE International Symposium on Geoscience and Remote Sensing IGARSS p.1484-1487
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Improving random forest with ensemble of features and semi-supervised feature extraction

Junshi Xia, Wenzhi Liao (UGent) , Jocelyn Chanussot, Peijun Du, Guanghan Song and Wilfried Philips (UGent)

(2015) IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. 12(7). p.1471-1475

Academic Bibliography

Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution

Foreword to the special Issue on Hyperspectral remote sensing and imaging spectroscopy

Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping

Remote sensing data fusion : guided filter-based hyperspectral pansharpening and graph-based feature-level fusion

Information extraction from remote sensing imagery

Taking optimal advantage of fine spatial information : promoting partial image reconstruction for the morphological analysis of very-high-resolution images

Fusion of spectral and spatial information for classification of hyperspectral remote-sensed imagery by local graph

Morphological attribute profiles with partial reconstruction

Lidar information extraction by attribute filters with partial reconstruction

Improving random forest with ensemble of features and semi-supervised feature extraction

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