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Super-Resolution for Jilin-1 Satellite Video Imagery via a Convolutional Network

机译:通过卷积网络实现吉林-1卫星视频图像的超分辨率

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摘要

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.
机译:卫星视频的超分辨率对于地球观测的准确性非常重要,视频卫星上特殊的成像和传输条件对该任务提出了巨大挑战。现有的基于深度卷积神经网络的方法需要对预处理或后处理进行调整,以适应高分辨率尺寸或像素格式,从而导致性能降低和额外复杂性。为此,本文提出了一种五层的端到端网络结构,无需进行任何预处理和后处理,但在网络末端施加了重塑或反卷积层,以保持地面物体在网络中的分布。图片。同时,我们通过组合非线性映射网络的输出和高维特征来制定联合损失函数,以精确学习低分辨率图像与其高分辨率对应物之间的理想映射关系。另外,我们将卫星视频数据本身用作训练集,这有利于训练和测试图像之间的一致性,并提高了该方法的实用性。在“吉林1号”卫星视频图像上的实验结果表明,该方法在视觉效果和测量指标方面均优于竞争方法。

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