  Cloud segmentation plays a crucial role in image analysis for climate
modeling. Manually labeling the training data for cloud segmentation is
time-consuming and error-prone. We explore to train segmentation networks with
synthetic data due to the natural acquisition of pixel-level labels.
Nevertheless, the domain gap between synthetic and real images significantly
degrades the performance of the trained model. We propose a color space
adaptation method to bridge the gap, by training a color-sensitive generator
and discriminator to adapt synthetic data to real images in color space.
Instead of transforming images by general convolutional kernels, we adopt a set
of closed-form operations to make color-space adjustments while preserving the
labels. We also construct a synthetic-to-real cirrus cloud dataset SynCloud and
demonstrate the adaptation efficacy on the semantic segmentation task of cirrus
clouds. With our adapted synthetic data for training the semantic segmentation,
we achieve an improvement of 6:59% when applied to real images, superior to
alternative methods.
