Unsupervised Change Detection in Satellite Images with Generative Adversarial Network
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Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with very high spatial resolution (VHR) and made it challenging to apply image coregistration whose accuracy is the basis of many change detection methods.Due to the advantage in deep feature representation, deep learning is introduced to detect changes on unregistered images. However, the absence of ground truth makes the performance of deep learning models in unsupervised task hard to be evaluated or be guaranteed.To alleviate the effect of unregistered pairs and make better use of deep learning structures, we propose a novel change detection procedure based on a special neural network architecture—Generative Adversarial Network (GAN).GAN features generating realistic images rather than giving hypervectors that contain visual features, so it is easy to evaluate the GAN model by judging the generated images. In this paper, we show that GAN model can be trained upon a pair of images through utilizing the proposed expanding strategy to create a training set and optimising designed objective functions. The optimised GAN model would produce many coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly.Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure.Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.
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