## 1 Introduction  
Farmland plays a critical role in global food security and sustainable development [1, 2]. Monitoring changes in farmland--such as expansion, contraction, or shifts in land use--is essential for effective land management, resource allocation, and environmental planning [3]. Consequently, detecting changes in farmland is significant for agricultural and environmental research, providing insights that can inform sustainable practices and policy decisions [4]. Given the extensive distribution of arable land, manual field surveys to track changes are often time-consuming and labor-intensive [5]. In this context, remote sensing image change detection has emerged as a crucial tool for monitoring environmental changes [6] and analyzing land cover alterations [7]. Among various change detection techniques, bi-temporal change detection stands out due to its simplicity and effectiveness, as it identifiesareas of significant change between two images captured at different times [8]. This method offers valuable information for decision-making in urban planning, agriculture, forestry, and natural resource management. However, despite advancements in change detection techniques for large-scale, high-resolution remote sensing images, challenges persist. Factors such as complex textures, seasonal variations, climate changes, and evolving demands continue to complicate the analysis of remote sensing data, making bi-temporal change detection one of the most demanding tasks in this field [9; 10]. In recent years, the increasing accessibility of high-resolution satellite imagery, coupled with significant advancements in image processing algorithms, has led to a burgeoning interest in bi-temporal change detection among researchers and practitioners alike. Over the past few decades, a variety of optical remote sensing image change detection methodologies have been developed to address the inherent challenges in this field. For instance, [PERSON] et al. [11] introduced the Morphological Building Index (MBI), which utilizes a series of morphological operations to automatically detect buildings in high-resolution imagery by capturing the intrinsic spectral characteristics of these structures, including brightness, contrast, and size. Similarly, [PERSON] et al. [12] employed Change Vector Analysis (CVA) and Principal Component Analysis (PCA) to facilitate land cover detection using multispectral data. Additionally, [PERSON] [13] and [PERSON] [14] explored multivariate change detection techniques, allowing for the compact representation and rapid extraction of change features. Despite the efficacy of traditional change detection methods utilizing handcrafted features in relatively simple scenes, they frequently encounter challenges in more complex environments [15]. The rapid development of artificial intelligence (AI) technologies and remote sensing platforms has propelled deep learning-based algorithms to the forefront, with them surpassing traditional methods in numerous applications. This progress is largely attributed to their powerful feature learning capabilities, adaptability, and stability. Currently, Convolutional Neural Networks (CNNs) and Transformers are the primary methods used for extracting discriminative features from data [16; 17]. CNNs have found widespread application across various tasks within the realm of computer vision, with their core strength residing in their ability to effectively capture local patterns and spatial hierarchies via local receptive fields. Notable models such as U-Net [18], DeepLab [19], and ResNet [20] exemplify the efficacy of CNNs in learning rich feature representations directly from raw data, making them particularly adept at processing two-dimensional images. However, the inherent constraint of CNNs lies in their local receptive fields, which limits their capability to capture long-range dependencies and global information.