# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 1 Introduction  
Change detection (CD) in remote sensing is the process of identifying surface modifications between bitemporal or more images [1]. The specific process can be seen in Figure 1; that is, some methods are used to distinguish the difference between two or more images at different times. It is essential for tracking dynamic changes on the Earth's surface [2], with wide-ranging applications in areas such as land use monitoring [3], disaster assessment [4], environmental change analysis [5], and urban expansion tracking [6]. Accurate and reliable CD methods are essential for timely decision-making in environmental protection, infrastructure planning, and emergency response [7]. Despite its importance, remote sensing change detection continues to be a challenging task due to various factors [8]. One primary issue is the influence of illumination variations, seasonal changes, and atmospheric conditions, which introduce noise and complicate the differentiation of genuine changes from environmental variations. Second, the spectral characteristics of changed and unchanged regions often overlap, leading to inter-class similarity and intra-class variability, which complicates the learning process. Third, many deep-learning-based CD models rely heavily on local pixel-wise differences, often failing to capture the broader spatial relationships necessary for robust change detection. Siamese-based architectures [9] have been widely employed for bitemporal image analysis, yetexisting models typically apply simple difference operations to extract changes, limiting their ability to distinguish subtle variations. Recent advancements in foundation vision models, particularly the SAM [10], have demonstrated remarkable generalization capabilities in segmentation tasks. SAM, designed as a promptable segmentation model, possesses strong feature extraction abilities and has shown impressive zero-shot performance across diverse datasets. However, its direct application to change detection is non-trivial due to several inherent limitations [11]. SAM, originally designed for natural image segmentation, lacks explicit mechanisms for modeling global contextual information and differential features, which are crucial for accurate change detection. Additionally, it does not inherently integrate global and differential representations, limiting its effectiveness in remote sensing applications [12]. Moreover, due to the inductive bias learned from natural image datasets [13], foundation models like SAM may struggle with domain adaptation when applied to remote sensing. Unlike natural images, remote sensing images exhibit unique spectral properties, large-scale geographic structures, and varied imaging conditions, which are not well captured by models trained on everyday scenes [14]. This domain gap can lead to suboptimal feature representations and reduced sensitivity to remote-sensing-specific changes. To overcome these limitations, we propose Siamese-SAM, a novel Siamese-based framework that leverages SAM as an encoder while introducing three dedicated modules to enhance both global and differential feature representations. The following summarizes the main contributions of our research:  
1. We introduce the Siamese-SAM model for detecting changes in remote sensing images, which fully leverages the advantages of the Siamese structure and SAM as an encoder. The Siamese structure effectively models bitemporal image dependencies, while SAM provides powerful feature extraction capabilities. This design enables rich feature representation learning and enhances the accuracy of change detection. 2. We designed the GIEM and the Differential Information Enhancement Module (DIEM) to enhance feature representations. GIEM strengthens global contextual understanding, improving the model's ability to capture large-scale scene changes. DIEM focuses on differential feature enhancement, ensuring more effective extraction of changed regions while reducing the interference of irrelevant variations. 3.