TIMF-Net: Temporal Interaction and Multiscale Fusion Networks for Remote Sensing Change Detection

Abstract

In recent years, the field of remote sensing change detection (RSCD) has experienced transformative advancements through the application of convolutional neural networks (CNNs). However, inconsistencies in image quality, noise, and pseudochanges caused by variations in illumination, climate, and surface conditions due to different acquisition times pose significant challenges. Addressing these issues, this study increases traditional RSCD methodologies by introducing a novel temporal interaction and multiscale fusion network (TIMF-Net). TIMF-Net incorporates a temporal interaction and difference enhancement module (TIDEM) that effectively extracts and augments change information within images. This module deeply integrates temporal information through a weighted fusion strategy, not only capturing the juxtaposition and superposition relationships between images but also unraveling complex feature representations to ensure accurate alignment and coupling of features across different periods. Additionally, we propose a multiscale global-aware (MSGA) module, which attends to both local details and global contextual information, integrating pixel-level features and demonstrating heightened sensitivity to multiscale changes such as path alterations, water fluctuations, and agricultural variations. TIMF-Net outperforms mainstream and state-of-the-art methods on three datasets, achieving an F1 score of 91.96% and intersection over union (IoU) of 85.12% on the LEVIR-CD dataset, an F1 of 93.37% and IoU of 87.56% on the WHU-CD dataset, and an F1 of 87.12% and IoU of 77.19% on the GZ-CD dataset, with 27.64 M Params and 42.8 G FLOPs.

Keywords

• Attention mechanism Transformer
• building change detection (BCD)
• dual graph-convolution module
• high-resolution remote sensing (RS) images