MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 7; p. 1269
• Main Authors: Ren, Wuxu, Wang, Zhongchen, Xia, Min, Lin, Haifeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Accuracy; Artificial intelligence; Change detection; CNN; Comparative analysis; Computer networks; Decoding; Deep learning; High resolution; Image processing; Image resolution; Machine learning; Methods; Morphology; Neural networks; Remote sensing; remote sensing images; Satellites; self-attention mechanism; Semantics; Support vector machines; Technology application; Temporal variations; transformer; China
• DOI: 10.3390/rs16071269
• ISSN: 2072-4292

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models.