  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.
