# Optimizing Data Processing in Space for Object Detection in Satellite Imagery

arXiv: https://arxiv.org/abs/2107.03774 · HTML: https://mlsanity.com/paper/2107.03774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [IoU-balanced Loss Functions for Single-stage Object Detection](https://mlsanity.com/paper/1908.05641/) (2019)
- [Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1809.07196/) (2018)
- [Accelerating Deep Learning Applications in Space](https://mlsanity.com/paper/2007.11089/) (2020)

## Built on by (most cited first, 2 of 2)

- [IQUAFLOW: A new framework to measure image quality](https://mlsanity.com/paper/2210.13269/) (2022)
- [Object Detection performance variation on compressed satellite image datasets with iquaflow](https://mlsanity.com/paper/2301.05892/) (2023)

## Abstract (arXiv)

There is a proliferation in the number of satellites launched each year, resulting in downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making this an expensive process considering the large data sizes and that not all of the data is useful. This, coupled with the increasing demand for real-time data processing, has led to a growing need for on-orbit processing solutions. In this work, we investigate the performance of CNN-based object detectors on constrained devices by applying different image compression techniques to satellite data. We examine the capabilities of the NVIDIA Jetson Nano and NVIDIA Jetson AGX Xavier; low-power, high-performance computers, with integrated GPUs, small enough to fit on-board a nanosatellite. We take a closer look at object detection networks, including the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) models that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of execution time, memory consumption, and accuracy, and are compared against a baseline containing a server with two powerful GPUs. The results show that by applying image compression techniques, we are able to improve the execution time and memory consumption, achieving a fully runnable dataset. A lossless compression technique achieves roughly a 10% reduction in execution time and about a 3% reduction in memory consumption, with no impact on the accuracy. While a lossy compression technique improves the execution time by up to 144% and the memory consumption is reduced by as much as 97%. However, it has a significant impact on accuracy, varying depending on the compression ratio. Thus the application and ratio of these compression techniques may differ depending on the required level of accuracy for a particular task.