Abstract: Change detection is an important research area in remote sensing. To achieve accurate results, it is essential to extract multi-scale spatial information from images while filtering out noise. However, existing models lack this capability. Therefore, Multi-Scale Feature Gathering Network (MSFGNet) is proposed. Within MSFGNet, Bi-Temporal Image Multi-Level Fusion Module (BMF) is utilized to fuse bi-temporal remote sensing images. Additionally, Multi-Receptive Field Features Extraction Module (MRFE) is utilized to extract deep features. Within MRFE, Large Receptive Field Features Extraction Module (LRFE) and Multi-Scale Information Fusion Module (MSIF) are designed, which use large kernel convolution and dilated convolution respectively to capture spatial information with large receptive fields. Furthermore, Cross-Dimension Feature Sifting Fusion Module (CDFSF) is designed to sift noise from various dimensions, fusing valuable information. Across multiple public datasets, MSFGNet consistently achieves the best experimental results. The code can be accessed at \href{https://github.com/juncyan/msfgnet.git.