Object Detection performance variation on compressed satellite image datasets with iquaflow

TL;DR

This paper introduces iquaflow, a software tool for analyzing how image compression affects object detection performance on satellite images, demonstrating its utility through a case study on the DOTA dataset.

Contribution

The paper presents iquaflow, a novel tool for studying the impact of image quality modifications on model performance, with a case study on satellite image compression effects.

Findings

Optimal compression level identified for object detection

Model performance degrades with increased compression

iquaflow effectively evaluates image quality impact

Abstract

A lot of work has been done to reach the best possible performance of predictive models on images. There are fewer studies about the resilience of these models when they are trained on image datasets that suffer modifications altering their original quality. Yet this is a common problem that is often encountered in the industry. A good example of that is with earth observation satellites that are capturing many images. The energy and time of connection to the earth of an orbiting satellite are limited and must be carefully used. An approach to mitigate that is to compress the images on board before downloading. The compression can be regulated depending on the intended usage of the image and the requirements of this application. We present a new software tool with the name iquaflow that is designed to study image quality and model performance variation given an alteration of the image dataset. Furthermore, we do a showcase study about oriented object detection models adoption on a public image dataset DOTA Xia_2018_CVPR given different compression levels. The optimal compression point is found and the usefulness of iquaflow becomes evident.