Change detection as an interdisciplinary discipline in the field of computer vision and remote sensing at present has been receiving extensive attention and research. Due to the rapid development of society, the geographic information captured by remote sensing satellites is changing faster and more complex, which undoubtedly poses a higher challenge and highlights the value of change detection tasks. We propose MFDS-Net: Multi-Scale Feature Depth-Supervised Network for Remote Sensing Change Detection with Global Semantic and Detail Information (MFDS-Net) with the aim of achieving a more refined description of changing buildings as well as geographic information, enhancing the localisation of changing targets and the acquisition of weak features. To achieve the research objectives, we use a modified ResNet_34 as backbone network to perform feature extraction and DO-Conv as an alternative to traditional convolution to better focus on the association between feature information and to obtain better training results. We propose the Global Semantic Enhancement Module (GSEM) to enhance the processing of high-level semantic information from a global perspective. The Differential Feature Integration Module (DFIM) is proposed to strengthen the fusion of different depth feature information, achieving learning and extraction of differential features. The entire network is trained and optimized using a deep supervision mechanism. The experimental outcomes of MFDS-Net surpass those of current mainstream change detection networks. On the LEVIR dataset, it achieved an F1 score of 91.589 and IoU of 84.483, on the WHU dataset, the scores were F1: 92.384 and IoU: 86.807, and on the GZ-CD dataset, the scores were F1: 86.377 and IoU: 76.021. The code is available at https://github.com/AOZAKIiii/MFDS-Net

翻译：变化检测作为当前计算机视觉与遥感领域的交叉学科，正受到广泛关注与研究。随着社会快速发展，遥感卫星捕捉到的地理信息变化日益频繁且复杂，这无疑对变化检测任务提出了更高挑战，也凸显了其价值。我们提出MFDS-Net：融合全局语义与细节信息的多尺度特征深度监督遥感变化检测网络，旨在实现对变化建筑物及地理信息更精细化的描述，增强变化目标的定位能力与弱特征获取能力。为实现研究目标，采用改进的ResNet_34作为主干网络进行特征提取，并使用DO-Conv替代传统卷积以更好地关注特征信息之间的关联性，获得更优训练效果。提出全局语义增强模块（GSEM），从全局视角增强高层语义信息的处理能力；提出差分特征融合模块（DFIM），加强不同深度特征信息的融合，实现差分特征的学习与提取。整个网络采用深度监督机制进行训练与优化。MFDS-Net的实验结果超越了当前主流变化检测网络：在LEVIR数据集上F1分数达91.589、IoU达84.483；在WHU数据集上F1为92.384、IoU为86.807；在GZ-CD数据集上F1为86.377、IoU为76.021。代码已开源至https://github.com/AOZAKIiii/MFDS-Net