Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existing convolutional neural networks (CNNs) based approaches often struggle to capture long-range dependencies. Whereas recent transformer-based methods are prone to the dominant global representation and may limit their capabilities to capture the subtle change regions due to the complexity of the objects in the scene. To address these limitations, we propose an effective Siamese-based framework to encode the semantic changes occurring in the bi-temporal RS images. The main focus of our design is to introduce a change encoder that leverages local and global feature representations to capture both subtle and large change feature information from multi-scale features to precisely estimate the change regions. Our experimental study on two challenging CD datasets reveals the merits of our approach and obtains state-of-the-art performance.

翻译：变化检测（CD）是遥感（RS）领域的一项基础任务，旨在检测同一地理区域在不同时间点的语义变化。现有基于卷积神经网络（CNN）的方法通常难以捕捉长距离依赖关系。而近期基于Transformer的方法易受全局表征的支配，由于场景中物体的复杂性，可能限制其捕捉细微变化区域的能力。为解决这些局限，我们提出一种有效的孪生网络架构，用于编码双时相遥感图像中发生的语义变化。设计核心在于引入一种变化编码器，该编码器利用局部与全局特征表征，从多尺度特征中同时捕捉细微与显著的变化特征信息，从而精确估计变化区域。在两个具有挑战性的变化检测数据集上的实验研究揭示了本方法的优势，并取得了最先进的性能。