  Change detection (CD) is one of the most vital applications in remote
sensing. Recently, deep learning has achieved promising performance in the CD
task. However, the deep models are task-specific and CD data set bias often
exists, hence it is inevitable that deep CD models would suffer degraded
performance after transferring it from original CD data set to new ones, making
manually label numerous samples in the new data set unavoidable, which costs a
large amount of time and human labor. How to learn a transferable CD model in
the data set with enough labeled data (original domain) but can well detect
changes in another data set without labeled data (target domain)? This is
defined as the cross-domain change detection problem. In this paper, we propose
a novel deep siamese domain adaptation convolutional neural network (DSDANet)
architecture for cross-domain CD. In DSDANet, a siamese convolutional neural
network first extracts spatial-spectral features from multi-temporal images.
Then, through multi-kernel maximum mean discrepancy (MK-MMD), the learned
feature representation is embedded into a reproducing kernel Hilbert space
(RKHS), in which the distribution of two domains can be explicitly matched. By
optimizing the network parameters and kernel coefficients with the source
labeled data and target unlabeled data, DSDANet can learn transferrable feature
representation that can bridge the discrepancy between two domains. To the best
of our knowledge, it is the first time that such a domain adaptation-based deep
network is proposed for CD. The theoretical analysis and experimental results
demonstrate the effectiveness and potential of the proposed method.
