PFCF-Net: A Network Based on Progressive Feature Interaction and Cross-Scale Feature Fusion for Remote Sensing Change Detection

Abstract

There exist some challenges in accurately capturing temporal change information and efficiently aggregating multi-level information in the field of remote sensing change detection. In order to expand the detection’s receptive field and fully fuse complementary information across different hierarchical levels, we propose a network based on progressive feature interaction and cross-scale feature fusion(PFCF-Net). Specifically, PFCF-Net adopts a naive backbone network ResNet-18 for efficient feature extraction. The progressive feature interaction module utilizes dilated convolutions with different dilation rates to capture feature changes at various scales, effectively capturing a wide range of changes from macroscopic to detailed levels in remote sensing images. The cross-scale feature fusion module improves cross-attention mechanism with the assistance of disparity guidance and peer guidance. It reduces semantic ambiguity and spatial detail loss in detected change objects, allowing the model to more effectively focus its detection efforts on regions of interest. Through extensive comparative experiments on three benchmark datasets, PFCF-Net has surpassed several state-of-the-art change detection methods in terms of accuracy.