# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 1 Introduction  
Land use/cover change detection (CD) is a fundamental task of remote sensing (RS) image processing. The objective of CD is to detect pixels with a \"semantic change\" between multitemporal RS images acquired at different times and in the same area [1]. CD has a wide range of applications [2], such as environmental investigation, illegal construction identification, urbanization monitoring, land use/cover monitoring, damage assessment, disaster risk management, and land use planning. Benefiting from the rapid development of high spatial resolution and multitemporal RS Earth observations, high-resolution RS images have become the main data sources for CD research [2]. Although the fine contextual information and complex spatial characteristics provided by high-resolution images generate rich spatial details for land cover analysis, they face several serious challenges too [3], such as enhanced intra-class variabilities, confused spectral features of different objects, shadow, illumination conditions, camera motion, complex scenarios, misregistration error [1], etc. Due to the great advantages of deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular for solving CD tasks in the RS community, and a variety of state-of-the-art network architectures have been developed to address the above-mentioned problems [4]. For example, to reduce the influence of illumination variations and misregistration errors caused by changes in sunlight angles and/or shooting angles [1], [PERSON] and [PERSON] [5] constructed a novel Siamese-based spatial-temporal attention neural network in which a CD self-attention mechanism was introduced to model the spatial-temporal relationships of changes. At present, FCN (fully convolutional network) and transformers are two mainstream deep learning approaches and research directions proposed for CD. Modeling in CD has long been dominated by Siamese convolutional neural networks, beginning with UNet-based models, namely, FC-EF, FC-Siam-conc, and FC-Siam-diff [6], and their revolutionary performance on the classical CDD [4] and BCDD [1] datasets. Siamese FCN networks have evolved to become increasingly powerful through larger scales [7], denser connections [1], and semi-supervised architectures [8; 9], as well as more sophisticated forms of convolution [10] and loss functions [11]. A variety of FCN-based CD networks can be found in the literature, such as IFN [12], SNUNet-CD [13], SiUNet3+CD [1], DASNet [14], STANet [5], MRA-SNet [15], PSNet [10], Si-HRNet [16], ChangeSTAR [17], SSJLN [11], and so on. In recent years, transformer-based architectures have become increasingly popular in CD tasks, and they have achieved competitive performance compared to their FCN counterparts [18; 19]. By leveraging the multi-head self-attention mechanism, the transformer has larger receptive fields and is notable for its use of attention to model long-range dependencies in the data [20]. On the contrary, most advanced CD pipelines using FCNs are still struggling to relate long-range concepts in space-time because of their inherent limitation of the size of the receptive field [21]. [PERSON] et al. [19] pointed out that introducing a transformer to remote sensing CD tasks is a three-step procedure: first, split the bitemporal image into a few semantic tokens; second, use a transformer encoder to model contexts in compact token-based space-time so that high-level concepts of the change of interest can be represented by a few visual words/tokens [21]; third, feedback the learned context-rich tokens to the pixel space to refine the original features via a transformer decoder. However, when trained on smaller amounts of data, the performance of transformer-based architectures is slightly lower than that of their similarly sized convolutional neural network (CNN) counterparts [22], e.g., ResNets [19] and ConvNext [23]. The possible reasons may be: (1) Images usually have a strong local 2D structure, and spatially neighboring pixels are always highly correlated.