TUP.P9.4

ASSESSMENT OF PERFORMANCE OF TREE-BASED ALGORITHMS TO REDUCE ERRORS OF OMISSION AND COMMISSION IN CHANGE DETECTION

Peter Rasmussen, Accenture Federal Services, United States; Jenna Abrahamson, North Carolina State University, United States; Xiaojing Tang, James Madison University, United States; Owen Smith, Josh Gray, North Carolina State University, United States; Curtis Woodcock, Boston University, United States; Marc Bosch Ruiz, Accenture Federal Services, United States
Session:
TUP.P9: Advanced Methods for Change Detection Poster
Track:
Data Analysis
Location:
Poster Area 9
Presentation Time:
Tue, 18 Jul, 14:15 - 15:45 Pacific Time (UTC -8)
Session Chair:
Peter Rasmussen, Accenture Federal Services
Session Managers:
Ge Jiang and Lanying Wang and Ayoti Banerjee and Shubham Awasthi
Presentation
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Discussion
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Resources
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Session TUP.P9
TUP.P9.1: PROGRESSIVE SCALE-AWARE NETWORK FOR REMOTE SENSING IMAGE CHANGE CAPTIONING
Chenyang Liu, Jiajun Yang, Zipeng Qi, Zhengxia Zou, Zhenwei Shi, Beihang University, China
TUP.P9.2: IMAGE CHANGE CAPTIONING ON REMOTE SENSING
Zhenghang Yuan, Lichao Mou, Xiaoxiang Zhu, Technical University of Munich, Germany
TUP.P9.3: EXCHANGE DATA AUGMENTATION FOR REMOTE SENSING IMAGE CHANGE DETECTION
Junyi Duan, Yijing Wang, Xu Tang, Jingjing Ma, Yuqun Yang, Xidian University, China
TUP.P9.4: ASSESSMENT OF PERFORMANCE OF TREE-BASED ALGORITHMS TO REDUCE ERRORS OF OMISSION AND COMMISSION IN CHANGE DETECTION
Peter Rasmussen, Accenture Federal Services, United States; Jenna Abrahamson, North Carolina State University, United States; Xiaojing Tang, James Madison University, United States; Owen Smith, Josh Gray, North Carolina State University, United States; Curtis Woodcock, Boston University, United States; Marc Bosch Ruiz, Accenture Federal Services, United States
TUP.P9.5: AN END TO END CHANGE DETECTION METHOD BASED ON DEEP SUPERVISION AND FEATURE MIXING FOR EROSION GULLY
Yingjie Tang, Harbin Engineering University, China; Mingrong Zhu, The 8th Research Academy of China State Shipbuilding Corporation, China; Shou Feng, Chunhui Zhao, Yuanze Fan, Yongqi Chen, Harbin Engineering University, China
TUP.P9.6: EDGE-GUIDED FEATURE DENSE FUSION NETWORK FOR REMOTE SENSING IMAGE CHANGE DETECTION
Hejun Luo, Jia Liu, Fang Liu, Wenhua Zhang, Jingxiang Yang, Liang Xiao, Nanjing University of Science and Technology, China
TUP.P9.7: A TRIPLET MULTI-TASK LEARNING NETWORK FOR SEMANTIC CHANGE DETECTION
Shan Dong, Yute Li, Yin Zhuang, He Chen, Liang Chen, Beijing Institute of Technology, China
TUP.P9.8: NOVEL H-UNET APPROACH FOR CROPLAND CHANGE DETECTION USING CLCD
Rashmi Bhattad, Gujarat Technological University, India; Vibha Patel, Vishwakarma Government Engineering College, India; Samir Patel, Pandit Deendayal Energy University, India
TUP.P9.9: SIAMESE RECURRENT RESIDUAL REFINEMENT NETWORK FOR HIGH-RESOLUTION IMAGE CHANGE DETECTION
Chengwei Huang, Ling Hu, Nanjing University of Science and Technology, China; Wenzhi Liao, Flanders Make and Ghent University, Belgium; Liang Xiao, Nanjing University of Science and Technology, China
TUP.P9.10: A HYPERSPECTRAL CHANGE DETECTION ALGORITHM BASED ON ACTIVE LEARNING STRATEGY
Yongqi Chen, Harbin Engineering University, China; Mingrong Zhu, The 8th Research Academy of China State Shipbuilding Corporation, China; Chunhui Zhao, Shou Feng, Yuanze Fan, Yingjie Tang, Harbin Engineering University, China
Resources
No resources available.