  Due to the various reasons such as atmospheric effects and differences in
acquisition, it is often the case that there exists a large difference between
spectral bands of satellite images collected from different geographic
locations. The large shift between spectral distributions of training and test
data causes the current state of the art supervised learning approaches to
output unsatisfactory maps. We present a novel semantic segmentation framework
that is robust to such shift. The key component of the proposed framework is
Color Mapping Generative Adversarial Networks (ColorMapGAN), which can generate
fake training images that are semantically exactly the same as training images,
but whose spectral distribution is similar to the distribution of the test
images. We then use the fake images and the ground-truth for the training
images to fine-tune the already trained classifier. Contrary to the existing
Generative Adversarial Networks (GANs), the generator in ColorMapGAN does not
have any convolutional or pooling layers. It learns to transform the colors of
the training data to the colors of the test data by performing only one
element-wise matrix multiplication and one matrix addition operations. Thanks
to the architecturally simple but powerful design of ColorMapGAN, the proposed
framework outperforms the existing approaches with a large margin in terms of
both accuracy and computational complexity.
