## 1 Introduction
proposed an attention-based Siamese network known as MFPNet, which leverages multi-path fusion and an adaptive weighted fusion strategy to integrate image features efficiently [33]. [PERSON] et al. introduced a change detection methodology based on a Differential Discriminative Network, employing an attention mechanism to fuse multi-level features with the image's difference map [37]. [PERSON] et al. contributed to the field by developing a Transformer-based architecture aimed at capturing global change features [38]. Finally, [PERSON] et al. proposed a hybrid Siamese network combining CNN and Transformer elements to enhance the accuracy of farmland change detection models [39]. Although existing work has made some progress, the issue of high missed detection rates in farmland change detection tasks remains a challenge. Efficiently and robustly modeling the multi-scale information between bi-temporal images by unifying global and local features in the feature encoding process is a pressing requirement for rapid farmland data collection. Inspired by recent advancements in change detection, we developed an attention-based multi-scale Siamese network for farmland change detection. This network combines the strengths of Convolutional Neural Networks (CNNs), Transformers, multi-scale modules, and attention mechanisms. By adopting a CNN-Transformer architecture, it achievesthe unified modeling of multi-scale global semantic information and local contour details in farmland while using local feature mixing techniques for denoising. The model structure is shown in the figure below. The contributions of this paper can be summarized in four key points:  
* A multi-scale channel information mixing module is designed within the convolutional network, which emphasizes local contours in remote sensing images and focuses on the denoising of farmland remote sensing images. Information loss across different scales and channels is also addressed. * A multi-head channel interaction and attention encoder is designed, enabling global context modeling based on key local contour information. * A multi-scale and attention-based spatial decoder is designed, which maps the one-dimensional vectors of local contour features and global semantic information--processed by the CNN feature mixing module and encoder--back into the spatial dimension. * The model is developed by combining the strengths of Convolutional Neural Networks (CNNs), Transformers, multi-scale modules, and attention mechanisms, effectively integrating attention mechanisms with Transformers. The model's performance is evaluated on two benchmark datasets: the Cropland Change Detection (CLCD) dataset [39], a high-resolution dataset specifically designed for cropland change monitoring, and the High-Resolution Semantic Change Detection (HRSCD) dataset [40], which focuses on general semantic change detection across diverse land cover types. A more detailed discussion of these datasets is provided in a later section of this paper. In our research, although an optimized method is proposed to address this challenge, significant pressure is still placed on the model's processing capabilities due to high-frequency image acquisition, especially when farmland changes are rapid and continuous. The high temporal frequency of remote sensing data brings a large amount of data and computational requirements, and existing models still have performance bottlenecks when processing large-scale continuous images. This situation is particularly prominent in the fields of land management and agricultural monitoring. Land managers often require real-time and high-frequency change detection results, yet the applicability of existing methods in this regard still needs further improvement.