Bilateral feature pyramid U-Net based on channel attention for fast change detection

This paper introduces a Channel Attention-based Bilateral Feature Pyramid U-Net (CABFPU-Net) for efficient change detection tasks in remote sensing. CABFPU-Net comprises three main networks: a backbone, a neck, and a head. The backbone, based on DenseNet, extracts primary features from input images. The neck network leverages channel attention to efficiently process multi-scale features, accentuating regions of change, and culminating in generation of multi-scale change attention features. This attention mechanism efficiently extracts relevant features by applying channel-wise attention, reducing dimensionality and enabling faster change detection. Finally, the head network integrates these features to produce a detailed change map. CABFPU-Net achieves a 53.9% reduction in processing time compared to CADNet on the LEVIR-CD dataset while maintaining F1-score of 91.3%, thereby demonstrating its efficiency and accuracy.