Intermediate Domain Prototype Contrastive Adaptation for Spartina alterniflora Segmentation Using Multitemporal Remote Sensing Images

作者：Boyu Zhao, Mengmeng Zhang, Wei Li, Xiukai Song, Yunhao Gao, Yuxiang Zhang, Junjie Wang · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3350691 · 被引用次数：24 · 研究领域：Flood Risk Assessment and Management、Domain Adaptation and Few-Shot Learning、Hydrological Forecasting Using AI

As an invasive plant in wetlands, Spartina alterniflora (S. alterniflora) causes immeasurable damage to wetland ecosystems. Observing S.alterniflora using multitemporal remote sensing data helps us better understand its further development and facilitates effective containment of its invasion trend. However, inconsistent representation across remote sensing data from different time periods poses a challenge. Fortunately, the utilization of unsupervised domain adaptation (UDA) techniques helps in addressing such issues and enables the exploration of rich temporal dimension information in multitemporal remote sensing data, revealing the spatio-temporal distribution characteristics of S.alterniflora. However, existing UDA methods mostly focus on directly aligning the global or intraclass distribution representations across domains, which overlooks the issue of significant differences between extreme domains and lacks exploration of interclass relationships. To address these limitations, an intermediate domain prototype class-level learning network (IDPNet) is proposed. IDPNet utilizes dynamically generated intermediate domain (ID) features to construct class prototypes while incorporating interclass information into the prototype construction, achieving the class-centered distribution alignment for adaptation. Moreover, intermediate domain feature generation module (IFM) is employed in IDPNet to blend the latent representations from various domains and generate ID features in re...