MCECF: A Multiscale Complementary Enhanced Context Fusion Network for Remote Sensing Change Detection

Abstract

Remote sensing change detection (RSCD) holds significant research value in remote sensing (RS) image processing. In recent years, many researchers have achieved remarkable results in RSCD tasks using methods based on convolutional neural networks (CNNs) or Transformers. Considering the limited receptive field of CNN models and the high computational cost of Transformers, many researchers have combined the two approaches, yielding promising results. However, most current RSCD-based models focus solely on change and temporal information, overlooking their complementary relationship. Additionally, some multiscale feature fusion methods emphasize enhancing individual scales while neglecting the correlations between different scales. To address the above issues, we propose a multiscale complementary enhanced context fusion (MCECF) network. The network first introduces a global-local context aggregation module (GLCAM) to capture global-local context information while extracting multilevel feature maps. Subsequently, a complementary enhancement difference module (CEDM) is employed to complementarily aggregate the captured change and temporal information of bi-temporal RS image features. To fully leverage the correlations between multiscale features, a progressive decoder comprising a supervised spatial attention (SSA) mechanism and a multiscale complementary enhanced fusion module (MCEFM) was developed. Moreover, to tackle the disparity between changed and unchanged regions, a dual-branch dynamic attention fusion module (DAFM) was designed to enhance the model’s adaptability to diverse scenarios. We conducted comparative experiments on five RSCD datasets against nine state-of-the-art (SOTA) methods, and the results confirmed the effectiveness of the proposed MCECF in RSCD tasks. Our code will be made available at https://github.com/kakuqikaduo/MCECF