Densely Labeling Large-Scale Satellite Images with Generative Adversarial Networks

Building an efficient and accurate pixel-level labeling framework for large-scale and high-resolution satellite imagery is an important machine learning application in the remote sensing area. Due to the very limited amount of the ground-truth data, we employ a well-performing superpixel tessellation approach to segment the image into homogeneous regions and then use these irregular-shaped regions as the foundation for the dense labeling work. A deep model based on generative adversarial networks is trained to learn the discriminating features from the image data without requiring any additional labeled information. In the subsequent classification step, we adopt the discriminator of this unsupervised model as a feature extractor and train a fast and robust support vector machine to assign the pixel-level labels. In the experiments, we evaluate our framework in terms of the pixel-level classification accuracy on satellite imagery with different geographical types. The results show that our dense-labeling framework is very competitive compared to the state-of-the-art methods that heavily rely on prior knowledge or other large-scale annotated datasets.