RaHFF-Net: Recall-adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection

Remote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. For complex scenarios (e.g. varying lighting conditions), the diverse shapes and scales of the changed areas is especially vulnerable to cause CD models to suffer from serious missed detections. To address aforementioned problem, we propose a high recall multi-scale feature fusion model for RS change interpretation. Initially, the RaHFF-Net extracts hierarchical multi-scale feature from bitemporal RS images; Then, it employs CNN and Transformer to effectively merge local and global information across same-scale, cross-scale, and multi-scale features; Finally, to address the issue of instance imbalance in change detection, a novel hyper expectation push pull (HEPP) loss regularization term is proposed. This loss function is designed to elevate the expected predictions of positive instances across the dataset, thereby enabling the development of a deep learning model with a high recall rate.The code will be available at
https://github.com/iceking111/RaHFF-Net
.