  Deep learning for change detection is one of the current hot topics in the
field of remote sensing. However, most end-to-end networks are proposed for
supervised change detection, and unsupervised change detection models depend on
traditional pre-detection methods. Therefore, we proposed a fully convolutional
change detection framework with generative adversarial network, to conclude
unsupervised, weakly supervised, regional supervised, and fully supervised
change detection tasks into one framework. A basic Unet segmentor is used to
obtain change detection map, an image-to-image generator is implemented to
model the spectral and spatial variation between multi-temporal images, and a
discriminator for changed and unchanged is proposed for modeling the semantic
changes in weakly and regional supervised change detection task. The iterative
optimization of segmentor and generator can build an end-to-end network for
unsupervised change detection, the adversarial process between segmentor and
discriminator can provide the solutions for weakly and regional supervised
change detection, the segmentor itself can be trained for fully supervised
task. The experiments indicate the effectiveness of the propsed framework in
unsupervised, weakly supervised and regional supervised change detection. This
paper provides theorical definitions for unsupervised, weakly supervised and
regional supervised change detection tasks, and shows great potentials in
exploring end-to-end network for remote sensing change detection.
