Dictionary Attention-Weighted Cross-Domain Contrastive Learning for Remote Sensing Image Change Detection  

作　　者：Tian Wei Youfa Liu Rui Zhao 

机构地区：[1]School of Computer Science,Wuhan University,Wuhan,430072,China [2]Faculty of Electrical Engineering and Computer Science,Ningbo University,315211,China

出　　处：《Data Intelligence》2024年第4期893-908,共16页数据智能(英文)

基　　金：supported in part by National Natural Science Foundation of China under Grants 62106081 and 42301376;in part by Project 2042024fg0013 supported by“the Fundamental Research Funds for the Central Universities”

摘　　要：Significant progress has been made in remote sensing image change detection due to the rapid development of Deep Learning techniques. Convolutional neural networks(CNNs) have become foundational models in this field. Previous works on remote sensing image change detection has utilized domain adaptation methods, achieving promising predictive performance. However, the transferable knowledge between source and target domain has not been fully exploited. In this paper, we propose a novel cross-domain contrastive learning approach for remote sensing image change detection, which correlates source and target domain using contrastive principles. Specifically, we introduce a transferable cross-domain Dictionary Learning scheme where a shared dictionary between the source and target domains generates sparse representations. Based on these representations, we compute attention weights and propose an attention-weighted contrastive loss to enhance knowledge transfer between source and target domains. Experiments demonstrate the effectiveness of the proposed methods on public remote sensing image change detection datasets.

关 键 词：Change detection Domain adaptation Contrastive learning Dictionary Learning Attention mechanism 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]