MFDS-Net: Multiscale Feature Depth-Supervised Network for Remote Sensing Change Detection With Global Semantic and Detail Information

• Published in: IEEE geoscience and remote sensing letters Vol. 21; pp. 1 - 5
• Main Authors: Huang, Zhenyang, Fu, Zhaojin, Song, Jintao, Yuan, Genji, Li, Jinjiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attention mechanism; Change detection; Computer vision; Convolution; deep supervision; Depth; Feature extraction; Fuses; Image edge detection; Information processing; Localization; Modules; multiscale features; R&D; Remote sensing; Research & development; Semantics; Task complexity; Transformers
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3461957

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 a multiscale feature depth-supervised network (MFDS-Net) 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 localization of changing targets and the acquisition of weak features. To achieve the research objectives, we use a modified <inline-formula> <tex-math notation="LaTeX">\text {ResNet}_{34} </tex-math></inline-formula> as a backbone network to perform feature extraction. 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 an IoU of 84.483. The code is available at https://github.com/AOZAKIiii/MFDS-Net .