论文标题
视觉搜索超过数十亿个空中和卫星图像
Visual search over billions of aerial and satellite images
论文作者
论文摘要
我们提出了一个系统，用于进行数十亿个空中和卫星图像的视觉搜索。视觉搜索的目的是查找与查询图像在视觉上相似的图像。我们使用经过空中和卫星图像训练的卷积神经网络产生的512抽象视觉特征来定义视觉相似性。这些功能转换为二进制值以减少数据和计算要求。我们使用Google Cloud的BigTable（BigTable（可扩展数据库）服务，我们采用了基于哈希的搜索。以1米的像素分辨率搜索美国大陆，相当于大约20亿张图像，大约需要0.1秒。该系统可以在地球表面上实时视觉搜索，并在https://search.descarteslabs.com上获得交互式演示。
We present a system for performing visual search over billions of aerial and satellite images. The purpose of visual search is to find images that are visually similar to a query image. We define visual similarity using 512 abstract visual features generated by a convolutional neural network that has been trained on aerial and satellite imagery. The features are converted to binary values to reduce data and compute requirements. We employ a hash-based search using Bigtable, a scalable database service from Google Cloud. Searching the continental United States at 1-meter pixel resolution, corresponding to approximately 2 billion images, takes approximately 0.1 seconds. This system enables real-time visual search over the surface of the earth, and an interactive demo is available at https://search.descarteslabs.com.
