SEMI-SUPERVISED CHANGE DETECTION BASED ON GRAPHS WITH GENERATIVE ADVERSARIAL NETWORKS

被引：0
|
作者：
Liu, Junfu [1 ,2 ,3 ]
Chen, Keming [1 ,2 ]
Xu, Guangluan [1 ,2 ]
Li, Hao [1 ,2 ]
Yan, Menglong [1 ,2 ]
Diao, Wenhui [1 ,2 ]
Sun, Xian [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Elect, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Elect, Key Lab Network Informat Syst Technol NIST, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
关键词：
Change Detection; Semi-supervised Learning; Graph Model; Generative Adversarial Network;
D O I：
10.1109/igarss.2019.8898913
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
In this paper, we present a semi-supervised remote sensing change detection method based on graph model with Generative Adversarial Networks (GANs). Firstly, the multi-temporal remote sensing change detection problem is converted as a problem of semi-supervised learning on graph where a majority of unlabeled nodes and a few labeled nodes are contained. Then, GANs are adopted to generate samples in a competitive manner and help improve the classification accuracy. Finally, a binary change map is produced by classifying the unlabeled nodes to a certain class with the help of both the labeled nodes and the unlabeled nodes on graph. Experimental results carried on several very high resolution remote sensing image data sets demonstrate the effectiveness of our method.
引用
收藏
页码：74 / 77
页数：4
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