*Article* **MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images**

**Jinming Ma, Gang Shi \*, Yanxiang Li and Ziyu Zhao**

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; majinming@stu.xju.edu.cn (J.M.); liyanxiang@stu.xju.edu.cn (Y.L.); 107551901060@stu.xju.edu.cn (Z.Z.) **\*** Correspondence: shigang@xju.edu.cn; Tel.: +86-135-7999-8016

**Abstract:** One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the Earth's surface. A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks. First, a Feature Enhancement Module (FEM) is proposed. The FEM introduces Coordinate Attention (CA). The CA block embeds the position information into the channel attention to obtain the accurate position information and channel relationships of the remote sensing images. An updated feature map is obtained by using an element-wise summation of the input of the FEM and the output of the CA. The FEM enhances the feature representation in the network. Then, an attention-based Feature Fusion Module (FFM) is designed. It changes the previous idea of layer-by-layer fusion and chooses cross-layer aggregation. The FFM is to compensate for some semantic information missing as the number of layers increases. FFM plays an important role in the communication of feature maps at different scales. To further refine the feature representation, a Refinement Residual Block (RRB) is proposed. The RRB changes the number of channels of the aggregated features and uses convolutional blocks to further refine the feature representation. Compared with all compared methods, MAFF-Net improves the *F*1-Score scores by 4.9%, 3.2%, and 1.7% on three publicly available benchmark datasets, the CDD, LEVIR-CD, and WHU-CD datasets, respectively. The experimental results show that MAFF-Net achieves state-of-the-art (SOTA) CD performance on these three challenging datasets.

**Keywords:** remote sensing images; change detection; attention mechanism; cross-layer feature fusion
