A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Amal Altamimi,Belgacem Ben Youssef

doi:10.3390/s22010263

Amal Altamimi, Belgacem Ben Youssef

Open Access

https://doi.org/10.3390/s22010263

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Abstract

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.

Highlights

Hyperspectral Imaging (HSI) is an enabling technology for a variety of remote sensing applications related to intelligence, commerce, agriculture, military, and even humanitarian purposes
According to [7], the number of bands of recognized hyperspectral imagers is as follows: (1) as many as 316 bands are acquired by the two payloads carried in the Indian Hyperspectral Imaging Satellite (HySIS); (2) 240 bands are collected by the Italian space agency’s satellite called no other than PRISMA, for PRecursore IperSpettrale della Missione Applicativa; (3) 220 bands are collected by the Hyperion imager onboard NASA’s Earth Observation satellite (EO-1); and (4) 232 bands are acquired by the German mission, known as the Environmental Mapping and Analysis Program (EnMAP)
We present in this paper a systematic review study of hardware-accelerated compression algorithms for remotely sensed hyperspectral images spanning more than 21 years of research works published in recognized journals and conferences

Summary

Introduction

Hyperspectral Imaging (HSI) is an enabling technology for a variety of remote sensing applications related to intelligence, commerce, agriculture, military, and even humanitarian purposes. According to [7], the number of bands of recognized hyperspectral imagers is as follows: (1) as many as 316 bands are acquired by the two payloads carried in the Indian Hyperspectral Imaging Satellite (HySIS); (2) 240 bands are collected by the Italian space agency’s satellite called no other than PRISMA, for PRecursore IperSpettrale della Missione Applicativa; (3) 220 bands are collected by the Hyperion imager onboard NASA’s Earth Observation satellite (EO-1); and (4) 232 bands are acquired by the German mission, known as the Environmental Mapping and Analysis Program (EnMAP) These significant band acquisitions result in large three-dimensional hyperspectral images, which make their onboard compression mandatory, especially for small satellites where the platforms are confined to limited storage capacity, weight, and power budget

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