SAR2OPT: Image Alignment Between Multi-Modal Images Using Generative Adversarial Networks
SAR2OPT: Image Alignment Between Multi-Modal Images Using Generative Adversarial Networks
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DOI：
10.1109/igarss.2019.8898605
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
H. Toriya;A. Dewan;I. Kitahara
中科院分区：
文献类型：
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作者：
H. Toriya;A. Dewan;I. Kitahara
这项工作提出了一种用于多模态图像（例如，合成孔径雷达（SAR）和光学卫星图像）使用图像特征为基础的关键点匹配算法。在将匹配算法应用于多模态图像时，需要在对应位置处获得共同特征。然而，图像之间的特征的外观是不同的。我们通过使用生成对抗网络（GAN）将一个模态图像的外观转换为另一个模态图像来解决这个问题。在这项工作中，我们试图从SAR图像中生成光学图像作为一种方法来提取共同的功能。通过实验，我们证实，该方法可以估计准确的SAR和光学图像之间的对应关系。
This work proposes an image-alignment method for multi-modal images (e.g., synthetic aperture radar (SAR) and optical satellite images) using an image-feature-based keypoint-matching algorithm. In applying the matching algorithm to multi-modal images, common features need to be obtained at the corresponding positions. However, the appearances of features among images are different. We solve this issue by translating the appearance of one modal image to the other using generative adversarial networks (GANs). In this work, we attempt to generate optical images from SAR images as a way to extract common features. Through an experiment, we confirm that the proposed method can estimate accurate correspondences between SAR and optical images.