Hardware-Optimized Architecture of On-Board Registration for Remote-Sensing Images —Take SURF as an Example

Abstract

On-board processing has become an inevitable choice for the development of remote-sensing satellites. For the satellites with dual-line array asynchronous push-broom mode, image registration is crucial for pixel localization and alignment between dual-band strip data. However, due to the high computational complexity of feature detection and feature extraction, it is still a challenge to realize real-time registration for on-board edge devices with limited computing power and power consumption. Therefore, this article proposes a hardware-optimized architecture with high-performance and low-energy for on-board registration based on “ARM+FPGA”. On one hand, optimized methods for the image registration algorithm are proposed from both software and hardware perspectives, providing a solution for on-board registration. On the other hand, in the face of the current interface environment of satellites, a design scheme for versatile hardware architecture is proposed. It provides a foundational technical route for hardware deployment of on-board processing. Experimental results show that in this hardware-accelerated architecture, the average acceleration effect of registration algorithm can reach 15 times compared to the unoptimized version. In addition, the average power consumption is reduced by 60%, and the hardware resource utilization is less than 40%. Importantly, the algorithm's accuracy remains unaffected by these optimizations. The on-board intelligent processing payload deployed with this hardware architecture has been successfully launched and validated in February 2022 in the orbit of the MN200Sar-1 satellite from China. It improves the real-time capability for on-board processing, and aims to achieve a new imaging mechanism where target and information transmission replace strip image transmission.

Keywords

• Field programmable gate array (FPGA)
• hardware acceleration
• high-level synthesis (HLS)
• on-board processing
• speed-up robust features (SURF)