Optical Satellite Image Change Detection Via Transformer-Based Siamese Network

Optical satellite image change detection is essential to monitor the use of Earth's resources. Convolutional neural networks(CNN)-based methods exhibit excellent performance on change detection. As Transformers became the de-facto standard in the field of natural language processing(NLP), there were more and more methods based on it are proposed in computer vision, such as image classification, object detection, semantic segmentation and so on. Many proposed models based on vision Transformer(ViT) have surpassed the performance of CNN and show effectiveness and superiority. With the emergence of more and more applications of ViT in the field of image processing, it's advantages are gradually being explored. In terms of change detection, the CNN-based models have already shown great advantages over traditional methods. In view of current achievements of Transformer, we decided to apply Transformer to change detection in optical satellite image. Change detection of bitemporal images, we need to take two images as inputs. So we proposed a Siamese extensions of ViT networks which achieve the best results in tests on two open change detection datasets. Experimental results on real datasets show the effectiveness and the superiority of the proposed network.