M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote Sensing Change Detection

Abstract

Remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>F<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods.