Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
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发表时间：
2021-06
影响因子：
6.4
通讯作者：
Yutong He;Dingjie Wang;Nicholas Lai;William Zhang;Chenlin Meng;M. Burke;D. Lobell;Stefano Ermon-Stefano
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文献类型：
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作者：
Yutong He;Dingjie Wang;Nicholas Lai;William Zhang;Chenlin Meng;M. Burke;D. Lobell;Stefano Ermon-Stefano
事实证明，高分辨率卫星图像可用于广泛的任务，包括测量全球人口、地方经济生计和生物多样性等。不幸的是，高分辨率图像很少收集，而且购买价格昂贵，因此很难在时间和空间上有效地扩展这些下游任务。我们提出了一个新的条件像素合成模型，使用丰富的，低成本的，低分辨率的图像生成准确的高分辨率图像的位置和时间，它是不可用的。我们表明，我们的模型达到了照片般逼真的样本质量，并在关键的下游任务-对象计数-特别是在地面条件迅速变化的地理位置上优于竞争基线。
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficiently and effectively scale these downstream tasks over both time and space. We propose a new conditional pixel synthesis model that uses abundant, low-cost, low-resolution imagery to generate accurate high-resolution imagery at locations and times in which it is unavailable. We show that our model attains photo-realistic sample quality and outperforms competing baselines on a key downstream task – object counting – particularly in geographic locations where conditions on the ground are changing rapidly.