Single-temporal Supervised Remote Change Detection for Domain Generalization

arXiv:2404.11326v1 Announce Type: new
Abstract: Change detection is widely applied in remote sensing image analysis. Existing methods require training models separately for each dataset, which leads to poor domain generalization. Moreover, these methods rely heavily on large amounts of high-quality pair-labelled data for training, which is expensive and impractical. In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization. Additionally, we propose a dynamic context optimization for prompt learning. Meanwhile, to …

abstract analysis arxiv change cs.cv data dataset detection domain image leads paper quality sensing temporal training training models type