Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1518-1522.DOI: 10.11772/j.issn.1001-9081.2018102083

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Detection of new ground buildings based on generative adversarial network

WANG Yulong1,2, PU Jun1,2, ZHAO Jianghua1,2, LI Jianhui1   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-10-15 Revised:2018-12-13 Online:2019-05-10 Published:2019-05-14
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (91546125), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020104).

基于生成对抗网络的地面新增建筑检测

王玉龙1,2, 蒲军1,2, 赵江华1,2, 黎建辉1   

  1. 1. 中国科学院 计算机网络信息中心, 北京 100190;
    2. 中国科学院大学, 北京 100049
  • 通讯作者: 黎建辉
  • 作者简介:王玉龙(1991-),男,河北邢台人,硕士研究生,主要研究方向:遥感图像处理、数据挖掘;蒲军(1994-),男,四川绵阳人,硕士研究生,主要研究方向:城市计算、交通数据挖掘;赵江华(1989-),女,河北保定人,助理研究员,博士研究生,主要研究方向:遥感数据处理与分析;黎建辉(1973-),男,湖北咸宁人,研究员,博士生导师,博士,主要研究方向:大数据资源开放共享、科学大数据管理、数据出版。
  • 基金资助:
    国家自然科学基金资助项目(91546125);中国科学院战略性先导科技专项(XDA19020104)。

Abstract: Aiming at the inaccuracy of the methods based on ground textures and space features in detecting new ground buildings, a novel Change Detection model based on Generative Adversarial Networks (CDGAN) was proposed. Firstly, a traditional image segmentation network (U-net) was improved by Focal loss function, and it was used as the Generator (G) of the model to generate the segmentation results of remote sensing images. Then, a convolutional neutral network with 16 layers (VGG-net) was designed as the Discriminator (D), which was used for discriminating the generated results and the Ground Truth (GT) results. Finally, the Generator and Discriminator were trained in an adversarial way to get a Generator with segmentation capability. The experimental results show that, the detection accuracy of CDGAN reaches 92%, and the IU (Intersection over Union) value of the model is 3.7 percentage points higher than that of the traditional U-net model, which proves that the proposed model effectively improves the detection accuracy of new ground buildings in remote sensing images.

Key words: Generative Adversarial Network (GAN), remote sensing image, change detection, image semantic segmentation, Focal loss

摘要: 针对传统的基于地物纹理和空间特征的方法很难精确识别地面新增建筑的问题,提出了一种基于生成对抗网络的新增建筑变化检测模型(CDGAN)。首先,使用Focal损失函数改进传统图像分割网络(U-net),并以此作为模型的生成器(G),用于生成遥感影像的分割结果;然后,设计了一个16层的卷积神经网络(VGG-net)作为鉴别器(D),用于区分生成的结果和人工标注(GT)的真实结果;最后,对生成器和判别器进行对抗训练,从而得到具有分割能力的生成器。实验结果表明,CDGAN模型的检测准确率达到92%,比传统U-net模型的平均区域重合度(IU)提升了3.7个百分点,有效地提升了遥感影像中地面新增建筑物的检测精度。

关键词: 生成对抗网络, 遥感影像, 变化检测, 图像语义分割, Focal损失

CLC Number: