用于基于一致性正则化的半监督变化检测的跨分支特征融合解码器
Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection
AI总结：
针对半监督变化检测中基于transformer的网络因标注数据不足而表现不佳的问题，提出跨分支特征融合解码器CBFF，结合局部卷积与全局transformer优势，基于强到弱一致性策略构建模型，在WHU-CD和LEVIR-CD数据集上优于七种先进方法。
摘要：
半监督变化检测（SSCD）利用部分标注数据和大量未标注数据来检测变化。然而，由于缺乏标注数据，基于transformer的SSCD网络表现不如基于卷积的SSCD网络。为克服这一局限，我们引入了一种名为Cross Branch Feature Fusion（CBFF）的新解码器，它结合了局部卷积分支和全局transformer分支的优势。卷积分支易于学习，能在少量标注数据下产生高质量特征。而transformer分支能提取全局上下文特征，但在没有大量标注数据时难以学习。利用CBFF，我们基于强到弱一致性策略构建了SSCD模型。通过在WHU-CD和LEVIR-CD数据集上的全面实验，我们证明了该方法优于七种最先进的SSCD方法。
英文摘要：
Semi-supervised change detection (SSCD) utilizes partially labeled data and a large amount of unlabeled data to detect changes. However, the transformer-based SSCD network does not perform as well as the convolution-based SSCD network due to the lack of labeled data. To overcome this limitation, we introduce a new decoder called Cross Branch Feature Fusion CBFF, which combines the strengths of both local convolutional branch and global transformer branch. The convolutional branch is easy to learn and can produce high-quality features with a small amount of labeled data. The transformer branch, on the other hand, can extract global context features but is hard to learn without a lot of labeled data. Using CBFF, we build our SSCD model based on a strong-to-weak consistency strategy. Through comprehensive experiments on WHU-CD and LEVIR-CD datasets, we have demonstrated the superiority of our method over seven state-of-the-art SSCD methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
