Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection
This work addresses the challenge of limited labeled data in change detection for remote sensing applications, representing an incremental improvement over existing methods.
The paper tackled the problem of semi-supervised change detection by introducing a Cross Branch Feature Fusion decoder that combines convolutional and transformer branches, achieving state-of-the-art results on WHU-CD and LEVIR-CD datasets.
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.