Object Detection performance variation on compressed satellite image datasets with iquaflow
This addresses a practical issue for industries using satellite imagery, where compression is necessary due to energy and time constraints, but it is incremental as it builds on existing object detection methods.
The paper tackles the problem of object detection model resilience when trained on compressed satellite images, presenting iquaflow as a tool to study image quality and model performance variation, with a showcase on the DOTA dataset finding an optimal compression point.
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.