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

TL;DR

This paper explores how deep learning-based image manipulation techniques like super resolution and cloud removal can enhance satellite imagery quality and speed up the sensor to shooter timeline, balancing effectiveness and processing time.

Contribution

It introduces the application of deep learning image manipulation methods to improve satellite imagery and analyzes their impact on the sensor to shooter timeline through simulation scenarios.

Findings

Super resolution improves image detail and clarity.

Cloud removal enhances information quality under cloud cover.

On-board processing can reduce overall sensor to shooter time.

Abstract

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.