期刊
SENSORS
卷 18, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/s18041194
关键词
super-resolution; video satellite; deep convolutional network
资金
- National Natural Science Foundation of China [61671332, 41771452, 41771454, U1736206]
- National Key R D Plan [2016YFE0202300]
- Hubei Province Technological Innovation Major Project [2017AAA123]
- Guangzhou Science and Technology Project [201604020070]
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.
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