  Recently, deep learning has achieved promising performance in the change
detection task. However, the deep models are task-specific and data set bias
often exists, thus it is difficult to transfer a network trained on one
multi-temporal data set (source domain) to another multi-temporal data set with
very limited (even no) labeled data (target domain). In this paper, we propose
a novel deep siamese domain adaptation convolutional neural network (DSDANet)
architecture for cross-domain change detection. In DSDANet, a siamese
convolutional neural network first extracts spatial-spectral features from
multi-temporal images. Then, through multiple 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, the
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 change
detection. The theoretical analysis and experimental results demonstrate the
effectiveness and potential of the proposed method.
