TU1.R2.1

AN UNSUPERVISED SIAMESE SUPERPIXEL-BASED NETWORK FOR CHANGE DETECTION IN HETEROGENEOUS REMOTE SENSING IMAGES

Zhiyuan Ji, Xueqian Wang, Zhihao Wang, Gang Li, Tsinghua University, China
Session:
TU1.R2: Deep Learning Methods for Change Detection I Oral
Track:
Data Analysis
Location:
Room C
Presentation Time:
Tue, 18 Jul, 08:30 - 08:42 Pacific Time (UTC -7)
Session Co-Chairs:
Gabriele Moser, University of Genoa and Jun Zhou, Griffith University
Session Manager:
Ge Jiang
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Session TU1.R2
TU1.R2.1: AN UNSUPERVISED SIAMESE SUPERPIXEL-BASED NETWORK FOR CHANGE DETECTION IN HETEROGENEOUS REMOTE SENSING IMAGES
Zhiyuan Ji, Xueqian Wang, Zhihao Wang, Gang Li, Tsinghua University, China
TU1.R2.2: WAVELET SIAMESE NETWORK FOR CHANGE DETECTION IN REMOTE SENSING IMAGES
Tianhan Li, Fengchao Xiong, Nanjing University of Science and Technology, China; Wenbin Zheng, Chengdu University of Information Technology, China; Zhuanfeng Li, Nanjing University of Science and Technology, China; Jun Zhou, Griffith University, Australia; Yuntao Qian, Zhejiang University, China
TU1.R2.3: SAS-NET: SIMILARITY ATTENTION SIAMESE NETWORK FOR BUILDING CHANGE DETECTION IN UAV IMAGES
Yikui Zhai, Wenba Li, Zijun Tan, Jianhong Zhou, Qing Li, Zilu Ying, Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China, China
TU1.R2.4: Rethinking Temporal-symmetry in Remote Sensing Change Detection
Haonan Guo, Xin Su, Bo Du, Chen Wu, Liangpei Zhang, Wuhan University, China
TU1.R2.5: MULTI-TEMPORAL IMAGES GENERATION FOR BUILDING CHANGE DETECTION PERFORMANCE PROMOTION
Yute Li, Shan Dong, Yin Zhuang, Beijing Institute of Technology, China; Hao Dong, Peking University, China; He Chen, Liang Chen, Beijing Institute of Technology, China
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