Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery
Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery
复制标题
估算超高分辨率俯视影像的空间分辨率
DOI：
10.1145/3356471.3365241
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发表时间：
2019-11
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
研究了高空图像空间分辨率的估计问题。在没有这种元数据的情况下，更多的架空图像变得可用，要么是因为它最初没有被收集，要么是因为它没有与图像一起保存。了解图像的空间分辨率对于目标检测、语义分割等一系列自动图像理解任务非常重要。在本文中，我们探索了一种带有特征提取前端和扩展卷积后端的回归框架来估计高架图像的空间分辨率。我们表明，堆叠式自动编码器前端的性能优于标准的卷积神经网络特征提取器。为了演示我们的方法，我们构建了一个评估数据集，由大量非常高分辨率的俯视图像组成，空间分辨率从每像素0.15米到1.0米不等。
We investigate the problem of estimating the spatial resolution of overhead imagery. 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. Knowing the spatial resolution can be important for a range of automated image understanding tasks such as object detection, semantic segmentation, etc. In this paper, we explore a regression framework with a feature extraction frontend and a dilated convolution backend to estimate the spatial resolution of an overhead image. We show that a stacked auto-encoder frontend outperforms a standard convolution neural network feature extractor. In order to demonstrate our approach, we construct an evaluation dataset consisting of a large collection of very high-resolution overhead images with spatial resolutions ranging from 0.15 to 1.0 meters per pixel.