Abstract
This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo3, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.
Highlights
The use of hyperspectral sensors on-board satellites is taking relevance for environmental studies.In the last years, Earth Observation (EO) space missions are incorporating this kind of sensors with identification and detection purposes
We propose the implementation of a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless compression standard [7] for multispectral and hyperspectral images
The Dynamic and Partial Reconfiguration (DPR) support in ARTICo3 allows different applications to coexist within the same Field-Programmable Gate Array (FPGA) device, which effectively reduces cost and enables system updates after deployment
Summary
The use of hyperspectral sensors on-board satellites is taking relevance for environmental studies. The space industry is integrating these SoCs in SmallSats for Low-Earth Orbit (LEO) missions where the radiation effects are reduced, to demonstrate the viability of this technology for short-time experiments [4] As it is well-known, radiation can cause errors in the FPGAs configuration memory, which may derive in a progressive or even complete malfunctioning of the device. This extension consists in adding a quantizer and a bit-rate feedback loop, to control the losses for achieving the targeted compression ratios without deteriorating in excess the quality of the decompressed image This solution has been developed using HLS techniques, and it is implemented over a reconfigurable, scalable and fault-tolerant architecture named ARTICo3 [8], which provides run-time adaptive computing performance, energy efficiency, and robustness against radiation when used as a payload processing system in short-time missions with COTS devices.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
