Session
Technical Session 1: Mission Operations and Autonomy
Location
Utah State University, Logan, UT
Abstract
With the cost of launch plummeting, it is now easier than ever to get a satellite to orbit. This has led to a proliferation of the number of satellites launched each year, resulting in the downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making it an expensive process considering only a small amount of it is useful. This, coupled with the increasing demand for real-time data, 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 powerful GPU cluster. The results from the initial experiments show that by applying image compression techniques, we are able to improve the execution time and memory consumption. 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 to as much as 97%. However, it has a significant impact on accuracy, varying depending on the compression ratio.
Optimizing Data Processing in Space for Object Detection in Satellite Imagery
Utah State University, Logan, UT
With the cost of launch plummeting, it is now easier than ever to get a satellite to orbit. This has led to a proliferation of the number of satellites launched each year, resulting in the downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making it an expensive process considering only a small amount of it is useful. This, coupled with the increasing demand for real-time data, 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 powerful GPU cluster. The results from the initial experiments show that by applying image compression techniques, we are able to improve the execution time and memory consumption. 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 to as much as 97%. However, it has a significant impact on accuracy, varying depending on the compression ratio.
