  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.
