  Traditional change detection methods usually follow the image differencing,
change feature extraction and classification framework, and their performance
is limited by such simple image domain differencing and also the hand-crafted
features. Recently, the success of deep convolutional neural networks (CNNs)
has widely spread across the whole field of computer vision for their powerful
representation abilities. In this paper, we therefore address the remote
sensing image change detection problem with deep learning techniques. We
firstly propose an end-to-end dual-branch architecture, termed as the W-Net,
with each branch taking as input one of the two bi-temporal images as in the
traditional change detection models. In this way, CNN features with more
powerful representative abilities can be obtained to boost the final detection
performance. Also, W-Net performs differencing in the feature domain rather
than in the traditional image domain, which greatly alleviates loss of useful
information for determining the changes. Furthermore, by reformulating change
detection as an image translation problem, we apply the recently popular
Generative Adversarial Network (GAN) in which our W-Net serves as the
Generator, leading to a new GAN architecture for change detection which we call
CDGAN. To train our networks and also facilitate future research, we construct
a large scale dataset by collecting images from Google Earth and provide
carefully manually annotated ground truths. Experiments show that our proposed
methods can provide fine-grained change detection results superior to the
existing state-of-the-art baselines.
