Learning color space adaptation from synthetic to real images of cirrus clouds
This addresses the problem of time-consuming manual labeling for climate modeling by enabling effective training with synthetic data, though it is incremental as it focuses on a specific domain adaptation technique.
The paper tackles the domain gap between synthetic and real images for cloud segmentation by proposing a color space adaptation method, achieving a 6.59% improvement in segmentation performance on real images.
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.