Change detection of VHR (Very High Resolution) images is very difficult due to the impacts caused by the seasonal
changes, the imaging condition, and so on. To address the above difficulty, a novel unsupervised change detection
algorithm is proposed based on deep learning, where the complex correspondence between the images is established by
Auto-encoder Model. By taking advantages of the powerful ability of deep learning in compensating the impacts
implicitly, the multi-temporal images can be compared fairly. Experiments demonstrate the effectiveness of the proposed
approach.
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