Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

02/12/2019

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by   Edward Collier, et al.

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Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93 traditional GAN training.