TU4.P6.3
CHANGEMACS: A MAMBA-BASED ATTENTION GUIDED AND CONTRASTIVE SIMILARITY LEARNING NETWORK FOR REMOTE SENSING CHANGE DETECTION
Kaixuan Jiang, Chen Wu, Wuhan University, China
Session:
TU4.P6: Multitask Learning and State Space Models for Change Detection Oral
Track:
Data Analysis
Location:
Plaza: Room P6
Presentation Time:
Tuesday, 5 August, 16:15 - 16:30
Session Chair:
Gabriele Moser, University of Genoa
Session TU4.P6
TU4.P6.1: CHANGEMAMBA: REMOTE SENSING CHANGE DETECTION WITH SPATIOTEMPORAL STATE SPACE MODEL
Hongruixuan Chen, Jian Song, The University of Tokyo, Japan; Chengxi Han, Intelligent Science & Technology Academy Limited of CASIC, China; Junshi Xia, RIKEN Center for Advanced Intelligence Project, Japan; Naoto Yokoya, The University of Tokyo, Japan
TU4.P6.2: SITSMamba for Crop Classification based on Satellite Image Time Series
Xiaolei Qin, Xin Su, Liangpei Zhang, Wuhan University, China
TU4.P6.3: CHANGEMACS: A MAMBA-BASED ATTENTION GUIDED AND CONTRASTIVE SIMILARITY LEARNING NETWORK FOR REMOTE SENSING CHANGE DETECTION
Kaixuan Jiang, Chen Wu, Wuhan University, China
TU4.P6.4: Cross-temporal and Spatial Information Fusion for Multi-task Building Change Detection using Multi-temporal Optical Imagery
Wen Xiao, Hui Cao, Yuqi Lei, Qiqi Zhu, Nengcheng Chen, China University of Geosciences, China
TU4.P6.5: CONTEXT SYNERGY AND MULTITASK INTERACTION NETWORK FOR SEMANTIC CHANGE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES
Siyu Yang, Hanchao Zhang, Xiaogang Ning, Ruiqian Zhang, Minghui Hao, Hao Wang, Chinese Academy of Surveying and Mapping, China; Qi Li, China Land Surveying and Planning Institute, China
Resources