GAN-Based SAR-to-Optical Image Translation with Region Information
GAN-Based SAR-to-Optical Image Translation with Region Information
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DOI：
10.1109/igarss39084.2020.9323085
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Kento Doi;Ken Sakurada;M. Onishi;A. Iwasaki
中科院分区：
文献类型：
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作者：
Kento Doi;Ken Sakurada;M. Onishi;A. Iwasaki
在本文中，我们提出了一种基于条件生成对抗网络（cGANs）的SAR到光学图像转换方法。虽然cGAN在图像翻译方面取得了巨大的成功，但SAR到光学图像的翻译仍然存在一些问题。其中一个问题是由于SAR数据中缺乏颜色信息而导致的彩色化误差。由于光学图像的颜色是多种多样的，而SAR图像没有颜色信息，生成器网络混乱，无法生成正确的彩色光学图像。为了防止这种情况，我们在图像平移网络中引入了区域信息。具体地说，来自预训练分类网络的特征向量被馈送到生成器和递归网络。SEN 1 -2数据集的实验结果表明，我们提出的方法比基线方法，不使用任何额外的信息的优势。
In this paper, we propose a SAR-to-optical image translation method based on conditional generative adversarial networks (cGANs). Though cGANs have achieved great success in image translation, some problems remain in SAR-to-optical image translation. One of the problems is the colorization error owing to the lack of color information in SAR data. Since the colors of optical images are varied, while SAR images have no color information, the generator network is confused and fail to generate correctly colorized optical images. To prevent it, we introduce a region information to the image translation network. Specifically, the feature vector from the pre-trained classification network is fed to the generator and discriminator network. Experimental results with SEN1-2 dataset show the advantage of our proposed method over the baseline method that does not use any additional information.