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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Deep Image- To-Image Transfer Applied to Resolution Enhancement of Sentinel-2 Images
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Deep Image- To-Image Transfer Applied to Resolution Enhancement of Sentinel-2 Images

机译：深度图像到图像的传输应用于Sentinel-2图像的分辨率增强

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摘要

Single Image Super-Resolution (SISR) is looking at restoring the missing high-resolution information from a single low-resolution image in order to increase the apparent spatial resolution by a factor of two or more. In recent years, convolution neural networks have been applied with great success to the problem of improving spatial resolution from a single image. With the advent of low-resolution (10 m) optical sensors such as Sentinel-2, it is interesting to explore the possibility of improving image resolution with Deep Learning (DL) techniques. The purpose of this article is to investigate the potential performances of recent DL super-resolution techniques. The techniques explored here include not only techniques for enhancing high-frequency content but also so-called image-to-image translation techniques based on Generative Adversarial Neural Networks (GAN). From our preliminary results, we show that GANs have the ability to restore complex textural information.
机译：单图像超分辨率（SISR）正在研究从单个低分辨率图像恢复丢失的高分辨率信息，以便将视在空间分辨率提高两倍或更多。近年来，卷积神经网络已成功应用于改善单个图像的空间分辨率的问题。随着诸如Sentinel-2之类的低分辨率（10 m）光学传感器的出现，探索通过深度学习（DL）技术提高图像分辨率的可能性非常有趣。本文的目的是研究最新的DL超分辨率技术的潜在性能。这里探讨的技术不仅包括增强高频内容的技术，还包括基于生成对抗神经网络（GAN）的所谓的图像到图像转换技术。从我们的初步结果来看，我们表明GAN具有恢复复杂纹理信息的能力。

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