Abstract
A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming; therefore, we propose a coarse-to-fine semi-supervised CD method based on consistency regularization (C2F-SemiCD), which includes a coarse-to-fine CD network with a multiscale attention mechanism (C2FNet) and a semi-supervised update method. Among them, the C2FNet network gradually completes the extraction of change features from coarse-grained to fine-grained through multiscale feature fusion, channel attention mechanism, spatial attention mechanism, global context module, feature refine module, initial aggregation module, and final aggregation module. The semi-supervised update method uses the mean teacher method. The parameters of the student model are updated to the parameters of the teacher Model by using the exponential moving average (EMA) method. Through extensive experiments on three datasets and meticulous ablation studies, including crossover experiments across datasets, we verify the significant effectiveness and efficiency of the proposed C2F-SemiCD method. The code will be open at: this https URL.
Abstract (translated)
为了检测变化（CD），高精度特征提取模型至关重要。在过去，许多基于深度学习的监督CD方法从大量带标签的时序图像中学习变化特征模式，而给定带标签的遥感图像的标注成本很高，并且通常需要花费很长时间。因此，我们提出了一个基于一致性正则化（C2F-SemiCD）的粗到细半监督CD方法，该方法包括具有多尺度注意机制（C2FNet）的粗到细CD网络和半监督更新方法。在这些方法中，C2FNet网络通过多尺度特征融合、通道关注机制、空间关注机制、全局上下文模块、特征优化模块、初始聚合模块和最终聚合模块，从粗粒度到细粒度完成了变化特征的提取。半监督更新方法使用平均教师方法。通过在三个数据集上进行广泛的实验和仔细的消融分析，包括跨数据集的交叉实验，我们证实了所提出的C2F-SemiCD方法的有效性和效率。代码将在这个链接上公开：https://this URL。
URL
https://arxiv.org/abs/2404.13838
