# Object Detection performance variation on compressed satellite image datasets with iquaflow

arXiv: https://arxiv.org/abs/2301.05892 · HTML: https://mlsanity.com/paper/2301.05892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 14 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Oriented R-CNN for Object Detection](https://mlsanity.com/paper/2108.05699/) (2021)
- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)
- [Gliding vertex on the horizontal bounding box for multi-oriented object detection](https://mlsanity.com/paper/1911.09358/) (2019)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [Learning RoI Transformer for Detecting Oriented Objects in Aerial Images](https://mlsanity.com/paper/1812.00155/) (2018)
- [Gaussian Bounding Boxes and Probabilistic Intersection-over-Union for Object Detection](https://mlsanity.com/paper/2106.06072/) (2021)
- [Optimizing Data Processing in Space for Object Detection in Satellite Imagery](https://mlsanity.com/paper/2107.03774/) (2021)
- [QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution](https://mlsanity.com/paper/2210.06618/) (2022)
- [IQUAFLOW: A new framework to measure image quality](https://mlsanity.com/paper/2210.13269/) (2022)

## Abstract (arXiv)

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.