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Current transformer-based change detection (CD) approaches either employ apre-trained model trained on large-scale image classification ImageNet datasetor rely on first pre-training on another CD dataset and then fine-tuning on thetarget benchmark. This current strategy is driven by the fact that transformerstypically require a large amount of training data to learn inductive biases,which is insufficient in standard CD datasets due to their small size. Wedevelop an end-to-end CD approach with transformers that is trained fromscratch and yet achieves state-of-the-art performance on four publicbenchmarks. Instead of using conventional self-attention that struggles tocapture inductive biases when trained from scratch, our architecture utilizes ashuffled sparse-attention operation that focuses on selected sparse informativeregions to capture the inherent characteristics of the CD data. Moreover, weintroduce a change-enhanced feature fusion (CEFF) module to fuse the featuresfrom input image pairs by performing a per-channel re-weighting. Our CEFFmodule aids in enhancing the relevant semantic changes while suppressing thenoisy ones. Extensive experiments on four CD datasets reveal the merits of theproposed contributions, achieving gains as high as 14.27\% inintersection-over-union (IoU) score, compared to the best-published results inthe literature. Code is available at\url{https://github.com/mustansarfiaz/ScratchFormer}.

Remote Sensing Change Detection With Transformers Trained from Scratch