论文标题
DEM的基于反馈神经网络的超级分辨率，用于产生高保真功能
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
论文作者
论文摘要
高分辨率数字高程模型（DEMS）是许多应用程序的重要要求，例如建模水流，滑坡，雪崩等。但是，公开可用的DEM在世界大部分地区都具有低分辨率。尽管使用深度学习解决方案在图像超级分辨率任务方面取得了巨大成功，但很少有作品在DEM上使用这些功能强大的系统来生成HRDEM。从反馈神经网络的动机中，我们提出了一种新型的神经网络体系结构，该架构学习将高频详细信息迭代地添加到低分辨率DEM，将其变成高分辨率DEM而不会损害其忠诚度。我们的实验证实，如果没有任何其他模式，例如航空图像（RGB），我们的网络DSRFB在4个不同的数据集中达到了0.59至1.27的RMSE。
High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images(RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets.
