C2F-SemiCD：基于一致性正则化的高分辨率遥感图像由粗到细半监督变化检测方法
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
AI总结：
针对遥感图像变化检测标注成本高的问题，提出基于一致性正则化的半监督方法C2F-SemiCD，通过多尺度注意力网络C2FNet与均值教师机制实现由粗到细的特征提取，在三个数据集上验证了其高效性。
摘要：
高精度特征提取模型对于变化检测（CD）至关重要。过去，许多基于深度学习的监督CD方法通过从大量标注的双时相图像中学习来识别变化特征模式，然而标注双时相遥感图像成本极高且通常十分耗时；因此，我们提出了一种基于一致性正则化的由粗到细半监督CD方法（C2F-SemiCD），该方法包含一个带有多尺度注意力机制的由粗到细CD网络（C2FNet）和一种半监督更新方法。其中，C2FNet网络通过多尺度特征融合、通道注意力机制、空间注意力机制、全局上下文模块、特征细化模块、初始聚合模块和最终聚合模块，逐步完成从粗粒度到细粒度的变化特征提取。该半监督更新方法采用均值教师方法。利用指数移动平均（EMA）方法，将学生模型的参数更新至教师模型的参数。通过在三个数据集上的大量实验及细致的消融研究（包括跨数据集的交叉实验），我们验证了所提出的C2F-SemiCD方法的显著有效性与高效性。代码将在以下地址开源：https://github.com/ChengxiHAN/C2F-SemiCDand-C2FNet。
英文摘要：
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: https://github.com/ChengxiHAN/C2F-SemiCDand-C2FNet.
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。
