Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Louisiana State University（路易斯安那州立大学）；Northeastern University（东北大学）；NASA Ames Research Center/BAERI（美国国家航空航天局艾姆斯研究中心/BAERI）；BAER Institute（BAER研究所）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
英文摘要：
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% compared to 89% for traditional GAN training.
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