Altamimi Amal, Ben Youssef Belgacem
Department of Computer Engineering, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
National Satellite Technology Center, Space and Aeronautic Research Institute, King Abdulaziz City for Science and Technology, P.O. Box 8612, Riyadh 12354, Saudi Arabia.
Sensors (Basel). 2021 Dec 30;22(1):263. doi: 10.3390/s22010263.
Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.
高光谱成像对于许多遥感应用来说是一项不可或缺的技术，但在计算资源方面成本高昂。由于高光谱数据量巨大，尤其是在传感器技术最近取得进展之后，它需要强大的处理能力和大量的存储空间。在将此类数据从机载卫星传输到地面站进行后处理时，还会出现与带宽限制相关的问题。这对于小型卫星应用尤为关键，因为其平台受到功率、重量和存储容量的限制。机载数据压缩技术的应用有助于减轻这些问题的影响，同时保留高光谱图像中包含的信息。本文对针对遥感应用的高光谱图像硬件加速压缩进行了系统综述。我们共查阅了2000年至2021年发表的101篇论文。我们对综合结果进行了比较性能分析，重点关注功率需求、吞吐量和压缩率等指标。此外，我们根据效率对最佳算法进行了排名，并阐述了影响硬件加速压缩性能的主要因素。最后，我们强调了文献中的一些研究空白，并推荐了未来研究的潜在领域。
