Enhanced Deep Learning Super-Resolution for Bathymetry Data
Enhanced Deep Learning Super-Resolution for Bathymetry Data
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DOI：
10.1109/bdcat56447.2022.00014
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Xingyan Li;Jian Li;Zachary Williams;Xin Huang;M. Carroll;Jianwu Wang
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作者：
Xingyan Li;Jian Li;Zachary Williams;Xin Huang;M. Carroll;Jianwu Wang
空间分辨率对于观察和监测环境现象至关重要。由于设备的限制，直接从卫星获取高分辨率的测深数据并不总是可行的，因此空间数据科学家和研究人员转向了单图像超分辨率(SISR)方法，该方法利用深度学习技术作为增加像素密度的替代方法。虽然超分辨率残差网络(例如SR-ResNet)在这方面很有希望，但仍然需要解决几个挑战：(1)诸如测深等地球数据的获取成本很高，并且其数据记录量相对有限；(2)在模型训练期间需要遵守某些领域的知识；(3)某些感兴趣的领域需要比其他领域更精确的测量。为了应对这些挑战，遵循迁移学习原理，我们研究了如何利用现有的预先训练的超分辨率深度学习模型，即SR-ResNet，来生成高分辨率的测深数据。我们进一步改进了SR-ResNet模型，增加了基于领域知识的相应损失函数。为了使模型对特定的空间区域有更好的表现，我们增加了额外的损失函数来增加对感兴趣区域的惩罚。我们的实验表明，当使用MSE、PSNR和SSIM等度量进行评估时，我们的方法获得了比大多数基线模型更高的精度。
Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.