# The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

## TL;DR

This study investigates how super-resolution techniques improve object detection accuracy in satellite imagery across various resolutions, demonstrating significant gains at finer resolutions and modest benefits at coarser levels.

## Contribution

It introduces a comprehensive evaluation of super-resolution effects on satellite object detection, combining VDSR and RFSR methods with a unified detection framework across multiple resolutions.

## Key findings

- Super-resolution improves detection accuracy at 30 cm resolution by up to 36%.
- Detection performance drops significantly as native resolution coarsens.
- Super-resolution to 15 cm yields the most substantial accuracy gains.

## Abstract

We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and super-resolved data, we then train several custom detection models using the SIMRDWN object detection framework. SIMRDWN combines a number of popular object detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed to rapidly detect objects in large satellite images. This approach allows us to quantify the effects of super-resolution techniques on object detection performance across multiple classes and resolutions. We also quantify the performance of object detection as a function of native resolution and object pixel size. For our test set we note that performance degrades from mean average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at coarser resolutions, though still provides a small improvement in performance.