TU2.R2.4

SPATIAL-PRESERVING AND EDGE-ORIENTING HIGH-RESOLUTION NETWORK FOR REMOTE SENSING CHANGE DETECTION

Jiaqi An, Fang Liu, Jia Liu, Liang Xiao, Xu Tang, Nanjing University of Science and Technology, China
Session:
TU2.R2: Deep Learning Methods for Change Detection II Oral
Track:
Data Analysis
Location:
Room C
Presentation Time:
Tue, 18 Jul, 10:51 - 11:03 Pacific Time (UTC -7)
Session Co-Chairs:
Chengxi Han, wuhan university and Haris Kontoes, Institute for Astronomy & Astrophysics, Space Applications and Remote Sensing National Observatory of Athens
Session Manager:
Yifan Yang
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Session TU2.R2
TU2.R2.1: BI-DIRECTIONAL RNN FOR SEMANTIC CHANGE DETECTION IN REMOTE SENSING IMAGES
Jing Zhang, University of Trento, Italy; Lei Ding, PLA Strategic Force Information Engineering University, China; Lorenzo Bruzzone, University of Trento, Italy
TU2.R2.2: M2-CDNET: A MULTI-SCALE AND MULTI-LEVEL NETWORK FOR REMOTE SENSING IMAGE CHANGE DETECTION
Qiong Wu, Zhi Zheng, Yi Wan, Yongjun Zhang, Wuhan University, China
TU2.R2.3: HCGMNET: A HIERARCHICAL CHANGE GUIDING MAP NETWORK FOR CHANGE DETECTION
Chengxi Han, Chen Wu, Bo Du, wuhan university, China
TU2.R2.4: SPATIAL-PRESERVING AND EDGE-ORIENTING HIGH-RESOLUTION NETWORK FOR REMOTE SENSING CHANGE DETECTION
Jiaqi An, Fang Liu, Jia Liu, Liang Xiao, Xu Tang, Nanjing University of Science and Technology, China
TU2.R2.5: GLOBAL-LOCAL REPRESENTATION COUPLING NETWORK FOR REMOTE SENSING IMAGE CHANGE DETECTION
Fanghan Yang, Xiangrong Zhang, Peng Zhu, Zhenhang Weng, Xiao Han, Puhua Chen, Xidian University, China
Resources
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