Abstract. The rapid development of the aerospace industry has significantly increased the demand for remote-sensing images with high resolution and quality. Generating images with expected resolution from the samples obtained by common acquisition devices is a challenging task as the trade-off between cost and efficiency must be considered. We propose a super-resolution (SR) algorithm especially for remote-sensing images that is based on generative adversarial networks optimized by a classifier, which is called classifier-based super-resolution generative adversarial network (CSRGAN). We hypothesize that the confidence scores of classification can be a critical factor for representing the features in target remote-sensing images. To sufficiently take this factor into account during training, we add the class-score as an error into the loss function in addition to mean square error and high-dimensional features extracted from deep neural networks. Then, the classifier is utilized for both better SR performance and more precise classification. The classifier-testing branch of our system can also be flexibly combined with other network architectures to optimize SR performance on remote-sensing images. We validate the model on the NWPU-RESISC45 dataset considering both SR and classification performance. The final analysis is also provided and shows that the proposed CSRGAN outperforms existing algorithms.

中文翻译：

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使用基于分类器的生成对抗网络的遥感图像超分辨率

摘要。航空航天工业的快速发展显着增加了对高分辨率和高质量遥感图像的需求。从常见采集设备获得的样本中生成具有预期分辨率的图像是一项具有挑战性的任务，因为必须考虑成本和效率之间的权衡。我们提出了一种基于由分类器优化的生成对抗网络的遥感图像的超分辨率（SR）算法，称为基于分类器的超分辨率生成对抗网络（CSRGAN）。我们假设分类的置信度分数可能是表示目标遥感图像中特征的关键因素。为了在训练中充分考虑这个因素，除了均方误差和从深度神经网络中提取的高维特征之外，我们将类分数作为误差添加到损失函数中。然后，分类器用于更好的 SR 性能和更精确的分类。我们系统的分类器测试分支也可以灵活地与其他网络架构结合，以优化遥感图像上的 SR 性能。我们在 NWPU-RESISC45 数据集上验证模型，同时考虑 SR 和分类性能。还提供了最终分析，并表明所提出的 CSRGAN 优于现有算法。我们系统的分类器测试分支也可以灵活地与其他网络架构结合，以优化遥感图像上的 SR 性能。我们在 NWPU-RESISC45 数据集上验证模型，同时考虑 SR 和分类性能。还提供了最终分析，并表明所提出的 CSRGAN 优于现有算法。我们系统的分类器测试分支也可以灵活地与其他网络架构结合，以优化遥感图像上的 SR 性能。我们在 NWPU-RESISC45 数据集上验证模型，同时考虑 SR 和分类性能。还提供了最终分析，并表明所提出的 CSRGAN 优于现有算法。