Semi-Supervised Change Detection With Fourier-Based Frequency Transformation

Semisupervised change detection (CD) methods have garnered increasing attention due to their capacity to alleviate the dependency of fully-supervised methods on a large number of pixel-level labels. These methods predominantly leverage generative adversarial network architecture and consistency regularization technology. However, they encounter challenges associated with background noise from cross-temporal images. In this article, we propose a novel multilevel consistency-regularization-based semisupervised CD approach that incorporates Fourier-based frequency transformation and a reliable pseudolabel selection scheme. Specifically, we replace the low-frequency spectrum of one temporal image with a frequency domain transformation derived from the corresponding image in the same bitemporal remote sensing image pair, enhancing the model's capability to discern meaningful changes amidst background noise, thereby contributing to more robust CD. Furthermore, excessively high pseudolabel thresholds in consistency regularization methods may result in the underutilization of valuable unlabeled data. To address this issue, we design a straightforward sigmoid-like function to dynamically adjust the selection threshold for the reliable pseudolabel selection scheme. This strategy takes into consideration the learning status throughout the entire training process, ensuring more effective utilization of unlabeled information. We demonstrate significant performance improvements across three widely-used public datasets, namely, LEVIR-CD, WHU-CD, and CDD. Notably, on the three datasets with only 1% labeled data, our method achieved an
$\text{IoU}^{c}$
of 71.29%, 63.90%, and 51.00%, outperforming existing state-of-the-art methods by 2.84%, 1.21%, and 0.98%, respectively. These results robustly substantiate the effectiveness of our approach, showcasing its potential in scenarios where labeled data is limited.