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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TU2.MM-4.5

A SUPERPIXEL AGGREGATION METHOD BASED ON MULTI-DIRECTION GRAY LEVEL CO-OCCURRENCE MATRIX FOR SAR IMAGE SEGMENTATION

Meiling Cui, Yulin Huang, Rufei Wang, Jifang Pei, Weibo Huo, Yin Zhang, Haiguang Yang, University of Electronic Science and Technology of China, China
Session:
Semantic Segmentation in SAR/PolSAR Data
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Tue, 13 Jul, 11:20-11:25 (UTC)
Tue, 13 Jul, 13:20-13:25 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 19:20-19:25 China Standard Time (UTC +8)
Tue, 13 Jul, 07:20-07:25 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Alireza Taravat, Deimos Space UK and Florence Tupin, Telecom Paris
Session Manager:
María Culman
Presentation
Discussion
Resources
Session TU2.MM-4
TU2.MM-4.1: Forest canopy mapping using synthetic aperture radar by means of pulse coupled neural networks
Alireza Taravat, Deimos Space UK, United Kingdom; Iraj Emadodin, Kiel University, Germany
TU2.MM-4.2: AN IMPROVED DARK-SPOT SEGMENTATION BASED ON NON-CIRCULARITY ENHANCED SAR IMAGERY: A PRELIMINARY EXPLORATION
Haitao Lang, Chenguang Ge, Wenjing Li, Shuangmei Zhao, Chunnan Li, Lihui Niu, Guang’an Yang, Beijing University of Chemical Technology, China
TU2.MM-4.3: Bayesian U-Net for Segmenting Glaciers in SAR Imagery
Andreas Hartmann, Amirabbas Davari, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein, Friendrich-Alexander Universität Erlangen-Nürnberg, Germany
TU2.MM-4.4: Glacier Calving Front Segmentation Using Attention U-Net
Michael Holzmann, Amirabbas Davari, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein, Friendrich-Alexander Universität Erlangen-Nürnberg, Germany
TU2.MM-4.5: A SUPERPIXEL AGGREGATION METHOD BASED ON MULTI-DIRECTION GRAY LEVEL CO-OCCURRENCE MATRIX FOR SAR IMAGE SEGMENTATION
Meiling Cui, Yulin Huang, Rufei Wang, Jifang Pei, Weibo Huo, Yin Zhang, Haiguang Yang, University of Electronic Science and Technology of China, China
TU2.MM-4.6: DEEP LEARNING BASED OIL SPILL CLASSIFICATION USING UNET CONVOLUTIONAL NEURAL NETWORK
Abdul Basit, Muhammad Adnan Siddique, Information Technology University (ITU), Pakistan; Muhammad Saquib Sarfraz, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), Germany
TU2.MM-4.7: OIL SPILL DETECTION BASED ON CBD-NET USING MARINE SAR IMAGE
Yanan Zhang, Qiqi Zhu, Qingfeng Guan, China University of Geosciences, China
TU2.MM-4.8: DISTRIBUTION CHARACTERISTICS OF GREEN ALGAE IN YELLOW SEA USING AN DEEP LEARNING AUTOMATIC DETECTION PROCEDURE
Yuan Guo, Le Gao, Xiaofeng Li, Institute of Oceanography, Chinese Academy of Sciences, China
TU2.MM-4.9: CLASSIFYING SEA ICE TYPES FROM SAR IMAGES USING A U-NET-BASED DEEP LEARNING MODEL
Yan Huang, Yibin Ren, Xiaofeng Li, Institute of Oceanology, Chinese Academy of Sciences and Center for Ocean Mega-Science, Chinese Academy of Sciences, China
TU2.MM-4.10: Convolutional Autoencoder for unsupervised representation learning of PolSAR Time-Series
Thomas Di Martino, ONERA, CentraleSupélec, Université Paris-Saclay, France; Régis Guinvarc'h, Laétitia Thirion-Lefevre, CentraleSupélec, France; Elise Colin Koeniguer, ONERA, Université Paris-Saclay, France

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