Planetary surface images are collected by sophisticated imaging devices onboard the orbiting spacecraft. Although these images enable scientists to discover and visualize the unknown, they often suffer from the ‘no-data’ region because the data could not be acquired by the onboard instrument due to the limitation in operation time of the instrument and satellite orbiter control. This greatly reduces the usability of the captured data for scientific purposes. To alleviate this problem, in this article, we propose a machine learning-based ‘no-data’ region prediction algorithm. Specifically, we leverage a deep convolutional neural network (CNN) based image inpainting algorithm to predict such unphotographed pixels in a context-aware fashion using adversarial learning on planetary images. The benefit of using our proposed method is to augment features in the unphotographed regions leading to better downstream tasks such as interesting landmark classification. We use the Moon and Mars orbital images captured by the JAXA's Kaguya mission and NASA's Mars Reconnaissance Orbiter (MRO) for experimental purposes and demonstrate that our method can fill in the unphotographed regions on the Moon and Mars images with good visual and perceptual quality as measured by improved PSNR and SSIM scores. Additionally, our image inpainting algorithm helps in improved feature learning for CNN-based landmark classification as evidenced by an improved F1-score of 0.88 compared to 0.83 on the original Mars dataset.COMP: Please replace colons appearing after figure numbers and table numbers with period in all figure and table captions.

中文翻译：

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通过轨道图像修复进行更好的行星表面探索

行星表面图像由轨道航天器上的精密成像设备收集。尽管这些图像使科学家能够发现和可视化未知数，但由于仪器和卫星轨道器控制的运行时间限制，机载仪器无法获取数据，因此它们经常受到“无数据”区域的影响。这大大降低了所捕获数据用于科学目的的可用性。为了缓解这个问题，在本文中，我们提出了一种基于机器学习的“无数据”区域预测算法。具体来说，我们利用基于深度卷积神经网络 (CNN) 的图像修复算法，使用行星图像上的对抗性学习，以上下文感知方式预测此类未拍摄像素。使用我们提出的方法的好处是增强未拍摄区域中的特征，从而导致更好的下游任务，例如有趣的地标分类。我们将 JAXA 的 Kaguya 任务和 NASA 的火星勘测轨道飞行器 (MRO) 捕获的月球和火星轨道图像用于实验目的，并证明我们的方法可以填充月球和火星图像上未拍摄的区域，并具有良好的视觉和感知质量。通过改进的 PSNR 和 SSIM 分数。此外，我们的图像修复算法有助于改进基于 CNN 的地标分类的特征学习，F1 分数为 0.88，而原始火星数据集的 F1 分数为 0.83。COMP：请用句点替换出现在图形编号和表格编号之后的冒号在所有图形和表格标题中。