Extraction of urban multi-class from high-resolution images using pyramid generative adversarial networks

The extraction of man-made structures from high-resolution images plays a vital role in various urban applications. This task is regularly complicated due to the heterogeneous appearance of the objects in the satellite images. In this work, we propose a multi-scale Generative Adversarial network to classify high-resolution images into urban classes (surface, building, tree, low-vegetation, car). We use uNet with EfficientNet B3 architecture as a generator and we use ResNet 18 architecture as a discriminator. We use a conditional generative loss based on the Dice coefficient and softmax functions. Experiments on the Vaihingen and Potsdam datasets were conducted to demonstrate the performance and we compare the results with other architectures. The results demonstrate the validity and higher performance of the proposed multi-scale network for extracting classes in urban areas with average F1-score 89.0% and 88.8%, and average accuracy 89.9% and 91.8% for Vaihingen and Potsdam datasets, respectively.