  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
