17 - 22 July, 2022 • Physical: Kuala Lumpur Convention Centre (KLCC), Kuala Lumpur, Malaysia • Hybrid: Online Platform

Preserving Our Heritage, Enabling Our Future Through Remote Sensing

17 - 22 July, 2022 • Physical: Kuala Lumpur Convention Centre (KLCC), Kuala Lumpur, Malaysia • Hybrid: Online Platform

TU7.V6.4

SPATIAL-SPECTRAL CONVOLUTIONAL SPARSE NEURAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING

Fengchao Xiong, Nanjing University of Science and Technology, China; Minchao Ye, China Jiliang University, China; Jun Zhou, Griffith University, Australia; Yuntao Qian, Zhejiang University, China
Session:
Optical Methods Oral Virtual
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Location:
Virtual Room 6
Presentation Time:
Tue, 19 Jul, 23:45 - 00:00 Malaysia Time (UTC +8)
Tue, 19 Jul, 17:45 - 18:00 Central European Time (UTC +2)
Tue, 19 Jul, 15:45 - 16:00 UTC
Tue, 19 Jul, 11:45 - 12:00 Eastern Time (UTC -4)
Session Co-Chairs:
John Kerekes, Rochester Institute of Technology and Han Van Nguyen, University of Iceland
Session Manager:
Moon Khiang Chin
Presentation
Discussion
Resources
No resources available.
Session TU7.V6
TU7.V6.1: HYPERSPECTRAL MIXED NOISE REDUCTION USING TWO-STAGE CASCADE REFINED NETWORK
Yinhu Wu, Junping Zhang, Dongyang Liu, Harbin Institute of Technology, China
TU7.V6.2: DEEP SPARSE AND LOW-RANK PRIOR FOR HYPERSPECTRAL IMAGE DENOISING
Han Van Nguyen, Magnús Örn Ulfarsson, Jakob Sigurðsson, Jóhannes Rúnar Sveinsson, University of Iceland, Iceland
TU7.V6.3: UNSUPERVISED DEEP HYPERSPECTRAL INPAINTING USING A NEW MIXING MODEL
Behnood Rasti, Pedram Ghamisi, Richard Gloaguen, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany
TU7.V6.4: SPATIAL-SPECTRAL CONVOLUTIONAL SPARSE NEURAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING
Fengchao Xiong, Nanjing University of Science and Technology, China; Minchao Ye, China Jiliang University, China; Jun Zhou, Griffith University, Australia; Yuntao Qian, Zhejiang University, China
TU7.V6.5: A Simple Modeling-approach For Mapping Submerged Aquatic Vegetation in Shallow Coastal Waters Based on Sentinel-MSI Data and Field Truth
Abderrazak Bannari, Space Pix-Map International, Canada; Thamer-Salim Ali, Asma Abahussain, Arabian Gulf University, Bahrain
TU7.V6.6: LEARNING SNOW LAYER THICKNESS THROUGH PHYSICS DEFINED LABELS
Debvrat Varshney, Maryam Rahnemoonfar, University of Maryland Baltimore County, United States; Oluwanisola Ibikunle, John Paden, University of Kansas, United States