  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) but made it
challenging to apply image coregistration, and many change detection methods
are dependent on its accuracy.Two images of the same scene taken at different
time or from different angle would introduce unregistered objects and the
existence of both unregistered areas and actual changed areas would lower the
performance of many change detection algorithms in unsupervised condition.To
alleviate the effect of unregistered objects in the paired images, we propose a
novel change detection framework utilizing a special neural network
architecture -- Generative Adversarial Network (GAN) to generate many better
coregistered images. In this paper, we show that GAN model can be trained upon
a pair of images through using the proposed expanding strategy to create a
training set and optimizing designed objective functions. The optimized GAN
model would produce better 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.
