Residential area extraction based on conditional generative adversarial networks
Residential area extraction based on conditional generative adversarial networks
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DOI：
10.1109/bigsardata.2017.8124931
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Fei Jin;Fan Wang;Jie Rui;Zhi Liu;Chao Wang;Hong Zhang
中科院分区：
文献类型：
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作者：
Fei Jin;Fan Wang;Jie Rui;Zhi Liu;Chao Wang;Hong Zhang
Automatic extraction of residential area from SAR image is a difficult task due to its complexity. The traditional methods based on segmentation or classification are effective. In this article, we present a novel method based on conditional generative adversarial networks (CGANs) to extract regular residential area in rural and urban region. CGANs is applied to extend the scope of research from supervised learning to semi supervised learning and adversarial training is used to improve the training effect. The experimental results show that the proposed method can achieve better results than traditional methods.
