SHIFT: Scribble-driven Hybrid Iterative Feature Tracker for Multimodal Remote Sensing Image Change Detection

Multimodal remote sensing change detection (MRS-CD) is essential for many geospatial applications. However, it remains challenging due to spectral heterogeneity, temporal misalignment, and inconsistencies between different data sources. Existing MRS-CD methods, particularly those employing graph structures, often suffer from parameter sensitivity, limited generalization across datasets, and difficulty in detecting fine-grained changes. Meanwhile, current weakly supervised approaches typically rely on image-level annotations, which often result in coarse localization, blurred boundaries, and limited transferability across heterogeneous modalities of changes. In this work, we present scribble-driven hybrid iterative feature tracker (SHIFT), a weakly supervised MRS-CD approach guided by sparse scribble annotations. SHIFT employs a scribble-driven feature extraction module to extract multimodal features, converting scribble annotations into smooth attention maps with a learnable Gaussian blur module to focus the model on potential change areas. Through iterative refinement, SHIFT enhances detection confidence in change regions. Experiments on 13 public datasets demonstrate the superior performance of the proposed approach compared with the number of state-of-the-art (SOTA) methods. Our approach achieves high accuracy with minimal annotations and robust generalization across diverse multimodal scenarios. The source code will be made publicly available at https://github.com/MissYongjie/SHIFT