Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks
Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks
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DOI：
10.1109/icip.2019.8802954
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Haolin Liang;S. Newsam
中科院分区：
文献类型：
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作者：
Haolin Liang;S. Newsam
我们专注于新的问题估计的空间分辨率的开销图像。越来越多的空中图像没有这种元数据，因为它不是在第一时间收集或没有保存的图像。我们提出了一种使用卷积神经网络的自下而上的数据驱动方法。我们表明，扩展模型，其中包括扩张卷积，以扩大网络的感受野优于基线模型的评估数据集与一系列的模拟空间分辨率。我们做了一些有趣的观察，以激励未来的工作在这个新的问题。
We focus on the novel problem of estimating the spatial resolution of overhead imagery. More and more overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. We propose a bottom-up, data-driven approach using convolutional neural networks. We show that an extended model which incorporates dilated convolution to expand the receptive field of the network outperforms a baseline model on an evaluation dataset with a range of simulated spatial resolutions. We make a number of interesting observations to motivate future work on this novel problem.