融合卷积与注意力机制，提升小样本下的变化检测精度
Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection
- 设计跨分支特征融合解码器，结合局部卷积与全局注意力优势
- 在WHU-CD和LEVIR-CD上优于7种先进方法，准确率显著提升
- 适合标签数据少但需高精度变化检测的遥感应用
半监督变化检测（SSCD）利用少量标注数据和大量未标注数据进行变化识别。然而，基于Transformer的SSCD网络因标注数据不足，性能不如基于卷积的模型。为解决此问题，本文提出一种新解码器——跨分支特征融合（CBFF），融合局部卷积分支与全局Transformer分支的优势。卷积分支易于学习，可在少量标注下生成高质量特征；Transformer分支能提取全局上下文信息，但需大量标注数据才能有效训练。通过构建基于强-弱一致性策略的SSCD模型，我们在WHU-CD和LEVIR-CD数据集上的实验表明，该方法优于七种前沿方法。
原文摘要 · Abstract (English)
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.
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