Abstract
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 this https URL
Abstract (translated)
目前，在计算机视觉和遥感领域，变化检测作为跨学科领域已经受到了广泛关注和研究。由于社会的快速发展，遥感卫星捕获的地理信息变化得更快、更复杂，无疑给变化检测任务带来了更高的挑战，同时也突出了变化检测任务的价值。我们提出了MFDS-Net：多尺度特征自监督网络用于远程 sensing变化检测全局语义和详细信息（MFDS-Net）作为目标，实现对变化建筑和地理信息的更精确描述，提高变化目标的定位，并获得弱特征的提取。为了实现研究目标，我们使用修改后的ResNet_34作为骨干网络进行特征提取，将DO-Conv作为传统卷积的替代方案，更好关注特征信息与特征信息的关联，以获得更好的训练结果。我们提出了全局语义增强模块（GSEM）用于从全局角度增强高级语义信息。差分特征融合模块（DFIM）提出了用于加强不同深度特征信息的融合，实现对差分特征的学习和提取。整个网络使用深度监督机制进行训练和优化。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，其得分达到了91.589，IoU为84.483；在WHU数据集上，得分分别为F1: 92.384和IoU: 86.807；在GZ-CD数据集上，得分分别为F1: 86.377和IoU: 76.021。代码可在此https://url
URL
https://arxiv.org/abs/2405.01065
