  Change detection in remote sensing images is an essential tool for analyzing
a region at different times. It finds varied applications in monitoring
environmental changes, man-made changes as well as corresponding
decision-making and prediction of future trends. Deep learning methods like
Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable
success in detecting significant changes, given two images at different times.
In this paper, we propose a Mamba-based Change Detector (M-CD) that segments
out the regions of interest even better. Mamba-based architectures demonstrate
linear-time training capabilities and an improved receptive field over
transformers. Our experiments on four widely used change detection datasets
demonstrate significant improvements over existing state-of-the-art (SOTA)
methods. Our code and pre-trained models are available at
https://github.com/JayParanjape/M-CD
