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.

提出了一种基于 Siamese 网络架构的改变检测方法，通过利用局部和全局特征表示捕捉多尺度特征，精确估计改变区域，实现了对遥感图像的语义变化的编码。在两个具有挑战性的改变检测数据集上的实验研究表明了该方法的优势，并获得了最先进的性能。