Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection
This work addresses robustness issues in remote sensing change detection for applications like environmental monitoring, though it is incremental as it builds on existing multimodal and vision-language approaches.
The paper tackles the problem of limited feature representation and robustness in remote sensing change detection by proposing MMChange, a multimodal method that fuses image and text modalities, achieving state-of-the-art performance on multiple datasets like LEVIRCD, WHUCD, and SYSUCD.
Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, and generalization especially under illumination and noise disturbances. To address this, we propose MMChange, a multimodal RSCD method that combines image and text modalities to enhance accuracy and robustness. An Image Feature Refinement (IFR) module is introduced to highlight key regions and suppress environmental noise. To overcome the semantic limitations of image features, we employ a vision language model (VLM) to generate semantic descriptions of bitemporal images. A Textual Difference Enhancement (TDE) module then captures fine grained semantic shifts, guiding the model toward meaningful changes. To bridge the heterogeneity between modalities, we design an Image Text Feature Fusion (ITFF) module that enables deep cross modal integration. Extensive experiments on LEVIRCD, WHUCD, and SYSUCD demonstrate that MMChange consistently surpasses state of the art methods across multiple metrics, validating its effectiveness for multimodal RSCD. Code is available at: https://github.com/yikuizhai/MMChange.