论文标题

使用深度学习来增强卫星图像,以示射击时间线

Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline

论文作者

Ciolino, Matthew, Hambrick, Dominick, Noever, David

论文摘要

射击时间线的传感器受两个主要变量的影响:卫星定位和资产定位。只有在有准备好的射手时,通过添加更多传感器或减少处理时间来加快卫星定位,否则主要时间来源是将射手置于位置。但是,情报界应致力于将传感器剥削至最高速度和有效性。在保持速度高的同时达到高效是一种权衡,这是传感器时间表中必须考虑的权衡。在本文中,我们研究了两个主要思想,从图像操纵中提高了卫星图像的有效性,以及在板上图像操纵如何影响传感器到射击时间线的有效性。我们在四种情况下介绍了这些想法:船上处理与地面电台处理的离散事件仿真,信息覆盖范围删除的信息质量,超级分辨率改进信息以及以图像为标题的数据减少。本文将展示图像操纵技术(例如超级分辨率,去除云和标题图像)如何提高交付信息的质量,除了显示这些过程如何影响传感器对射击时间线的影响。

The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decreasing processing time is important only if there is a prepared shooter, otherwise the main source of time is getting the shooter into position. However, the intelligence community should work towards the exploitation of sensors to the highest speed and effectiveness possible. Achieving a high effectiveness while keeping speed high is a tradeoff that must be considered in the sensor to shooter timeline. In this paper we investigate two main ideas, increasing the effectiveness of satellite imagery through image manipulation and how on-board image manipulation would affect the sensor to shooter timeline. We cover these ideas in four scenarios: Discrete Event Simulation of onboard processing versus ground station processing, quality of information with cloud cover removal, information improvement with super resolution, and data reduction with image to caption. This paper will show how image manipulation techniques such as Super Resolution, Cloud Removal, and Image to Caption will improve the quality of delivered information in addition to showing how those processes effect the sensor to shooter timeline.

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