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

Abstract

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 multiscale feature fusion model for RS change interpretation. Initially, the RaHFF-Net extracts hierarchical multiscale feature from bitemporal RS images; Then, it employs CNN and Transformer to effectively merge local and global information across same-scale, cross-scale, and multiscale features. Finally, to address the issue of instance imbalance in CD, a novel hyperexpectation push pull 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.

Keywords

• Change detection (CD)
• hyperexpectation push pull (HEPP) loss
• multiscale feature fusion
• transformer