Panoramic Change Analysis Framework (PCA-F): A New Method for Large-Scale Change Detection in High-Resolution Remote Sensing Images

In remote sensing image change detection (CD), leveraging distant contextual information is crucial for accuracy. Traditional neural network training, which involves image cropping, limits the perception of such information in large-scale images. To overcome this, we propose the "Panoramic Change Analysis Framework (PCA-F)" for high-resolution remote sensing CD. This innovative framework features a dual-branch architecture to integrate broad area information and uses advanced spatial alignment for merging wide-area and local features. PCA-F overcomes traditional method limitations and integrates well with various CD networks. The experimental results demonstrate a significant improvement in accuracy for the three baseline methods on two datasets (BIT: 3.51%, 4.66%; Changeformer: 1.49%, 2.64%; RDPNet: 1.97%, 5.31%), offering a new direction for enhancing CD performance in remote sensing imagery.