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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TH4.O-5.4

INTER-ORBIT CHANGE DETECTION FOR HIGH-RESOLUTION SAR IMAGERY USING CONDITIONAL SIAMESE NETWORK

Eiji Kaneko, Takahiro Toizumi, Kazutoshi Sagi, Masato Toda, NEC corporation, Japan
Session:
Innovative Approaches for Change Detection and Multi-temporal Analysis
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Thu, 15 Jul, 15:25-15:40 (UTC)
Thu, 15 Jul, 17:25-17:40 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 23:25-23:40 China Standard Time (UTC +8)
Thu, 15 Jul, 11:25-11:40 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Igor Yanovsky, Jet Propulsion Laboratory and Eiji Kaneko, NEC corporation
Session Manager:
Willeke A'Campo
Presentation
Discussion
Resources
Session TH4.O-5
TH4.O-5.1: CAPTIONING CHANGES IN BI-TEMPORAL REMOTE SENSING IMAGES
Seloua Chouaf, University of Sciences and Technology Houari Boumediene, Algeria; Genc Hoxha, University of Trento, Italy; Youcef Smara, University of Sciences and Technology Houari Boumediene, Algeria; Farid Melgani, University of Trento, Italy
TH4.O-5.2: BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION
David Alejandro Jimenez Sierra, Hernan Darío Benitez Restrepo, Universidad Pontificia Javeria, Colombia; Gonzalo R. Arce, Juan Felipe Flórez Ospina, University of Delaware, United States
TH4.O-5.3: Wildfire Detection Using Streaming Satellite Imagery
Steven Xu, Seunghyun Kong, Zohreh Asgharzadeh, SAS Institute Inc., United States
TH4.O-5.4: INTER-ORBIT CHANGE DETECTION FOR HIGH-RESOLUTION SAR IMAGERY USING CONDITIONAL SIAMESE NETWORK
Eiji Kaneko, Takahiro Toizumi, Kazutoshi Sagi, Masato Toda, NEC corporation, Japan
TH4.O-5.5: SPATIO-TEMPORAL SUPER-RESOLUTION RECONSTRUCTION OF REMOTE SENSING DATA
Igor Yanovsky, NASA Jet Propulsion Laboratory, California Institute of Technology, United States; Jing Qin, University of Kentucky, United States
TH4.O-5.6: TRIAL OF DETECTION ACCURACIES IMPROVEMENT FOR JJ-FAST DEFORESTATION DETECTION ALGORITHM USING DEEP LEARNING
Manabu Watanabe, Tokyo Denki University, Japan; Christian Koyama, Masato Hayashi, Izumi Nagatani, Takeo Tadono, Japan Aerospace Exploration Agency (JAXA), Japan; Masanobu Shimada, Tokyo Denki University, Japan

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