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

被引：20

作者：

Collier, Edward ^{[1
]}

Duffy, Kate ^{[2
]}

Ganguly, Sangram ^{[3
]}

Madanguit, Geri ^{[4
]}

Kalia, Subodh ^{[4
]}

Shreekant, Gayaka ^{[4
]}

Nemani, Ramakrishna ^{[5
]}

Michaelis, Andrew ^{[5
]}

Li, Shuang ^{[5
]}

Ganguly, Auroop ^{[2
]}

Mukhopadhyay, Supratik ^{[1
]}

机构：

[1] Louisiana State Univ, Baton Rouge, LA 70803 USA

[2] Northeastern Univ, Boston, MA 02115 USA

[3] NASA, Ames Res Ctr, BAERI, Mountain View, CA USA

[4] BAER Inst, Petaluma, CA USA

[5] NASA, Ames Res Ctr, Mountain View, CA USA

来源：

2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) | 2018年

关键词：

URBAN HEAT-ISLAND; WAVES;

D O I：

10.1109/ICDMW.2018.00115

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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 2dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training.

引用

页码：763 / 769

页数：7

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