Image Compression and Actionable Intelligence With Deep Neural Networks

TL;DR

This paper surveys various information reduction techniques, including neural network compression and object detection, to efficiently deliver satellite imagery to disadvantaged users with low connectivity.

Contribution

It introduces a comparative analysis of four different image reduction methods tailored for low-bandwidth satellite image delivery to edge devices.

Findings

Neural network compression offers significant size reduction.

Object detection cutouts preserve critical information.

Image captioning provides descriptive summaries.

Abstract

If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connectivity device on the edge. This case requires that we use a different approach to deliver intelligence, particularly satellite imagery information, than normally employed. To address this, we propose a survey of information reduction techniques to deliver the information from a satellite image in a smaller package. We investigate four techniques to aid in the reduction of delivered information: traditional image compression, neural network image compression, object detection image cutout, and image to caption. Each of these mechanisms have their benefits and tradeoffs when considered for a disadvantaged user.