## 1 Introduction
Similarly, for the purpose of solving the limitations of the CNN mentioned above, the Transformer has made considerable achievements in CD tasks [20; 21]. [PERSON] et al. [20] presented a method, where the Transformer was firstly brought into the CD task to enhance the spatial-temporal contextual information extraction capability through the Transformer module, and [PERSON] et al. [21] proposed a transformer-based siamese network architecture (abbreviated as ChangeFormer) for CD that united a hierarchical Transformer encoder to generate ConvNet-like multilevel features with a multilayer perceptron (MLP) decoder to effectively extract multiscale long-range relationships. However, these algorithms lacked some capture of local information, and the tight semantic features led to the loss of information such as contour. Based on the above problems, we realize that both local and global information are important, and the extraction of multiscale features is also an urgent work. SPNet [22] puts forward the feature enhancement and fusion module to fully explore the feature interaction between multimodal information and strengthen the feature communication between different scales, which has made good progress in salient object detection, which is a task to detect the most salient object. Inspired by SPNet [22], we determined to extend this structure into the field of CD and designed a New Fusion network with Dual-branch Encoder and Triple-branch Decoder (DETDNet). DETDNet adopts a codec architecture. The encoder is a dual-branch siamese structure, and the bitemporal image features are fused by a concise but effective module, namely concatenation and a \(1\times 1\) convolution operation (CAC). Moreover, the decoder uses a triple-branch structure, while using a refined Receptive Field Block (RFB) improved from [23; 24] to extract the multiscale contextual characteristics of the three branches, denoted as the multiscale feature extraction module (MFE), and to fuse the features of each layer in the middle branch with the next layer via the triple-branch aggregation (TA) module. Finally, the resolutions of the three change maps are recovered to be in accordance with the raw images after upsampling, and these three change maps are fused to build the final change map we need. The key work of this article unfolds in three ways:  
(1) DETDNet is proposed, in which the encoder is a dual-branch structure that captures the local features of images, and for the first time, a three-branch structure is used in the decoder to obtain multiscale features by using MFE. (2) We use different feature fusion methods for the decoder and encoder, respectively. The encoder applies CAC to fuse the bitemporal images taken in the same location at different times, and the decoder uses the TA module to fuse the triple-branch features. Futhermore, a cascade operation is adopted to fuse the features from the same stage in the encoder and decoder. (3) The experiments implemented on three publicly available datasets demonstrate our approach exceeds some recent approaches in terms of the F1 score, IoU, and OA. The rest of this paper is structured as follows. Section 2 lists related works. Section 3 shows the whole DETDNet structure and its details. Section 4 discusses the experiments conducted to provide evidence of the superiority of our approach, and Section 5 draws together the work of this paper.