An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images

Báscones, Daniel; González, Carlos Villaseca; Mozos, Daniel

doi:10.3390/rs12162563

Cited by 16 publications

(13 citation statements)

References 42 publications

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“…References [ 40 , 47 , 53 , 54 , 141 , 142 , 143 ] cover compression algorithms based on the JPEG2000 and JPEG-LS, with the first six employing the former and the last reference using the latter. The JPEG2000 standard offers two compression modes, lossy and lossless.…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

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

Altamimi

Youssef

2021

Sensors

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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.

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Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

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

Altamimi

Youssef

2021

Sensors

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“…Compression acceleration is also addressed in the paper "An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images" by Daniel Báscones, Carlos González, and Daniel Mozos [13], which derives a custom FPGA implementation of the costliest part (tier 1 coder within JPEG2000) of the JYPEC algorithm, a lossy hyperspectral compression algorithm that combines PCA and JPEG2000. The main goal is to accelerate it significantly to bring the full algorithm execution time down as much as possible and even below the real-time constraint.…”

Section: Overview Of the Issue: Remote Sensing Data Compressionmentioning

confidence: 99%

Editorial to Special Issue “Remote Sensing Data Compression”

Vozel

Serra-Sagristà

2021

Remote Sensing

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A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

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“…After mapping, ez(t) is outputted to the encoding module, and used to calculate the value of sgn, for the update of the weight vector Wz(t). The updated weight vector will be used in the next operation [19]. Figure 5 explains the workflow of the prediction module.…”

Section: Limitations Of Traditional Prediction Modulesmentioning

confidence: 99%

Design and Implementation of a Lossless Compression System for Hyperspectral Images

Fang¹,

Liu²,

Zhang³

2020

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Despite its popularity, the hyperspectral image compression algorithm recommended by the Consultative Committee for Space Data Systems (CCSDS) faces a long delay of the feedback loop and complex computations in the modes of band sequential (BSQ) and band interleaved by line (BIL). After analyzing the features of the CCSDS algorithm, this paper proposes a forward prediction method based on the xc7k325tffg9000 field programmable gate array (FPGA) chip (Xilinx Inc.), and adjusts the calculation flow of the CCSDS algorithm, aiming to shorten the time delay in the feedback loop. In addition, full-pipeline construction was implemented on FPGA board to realize real-time processing of data, and dynamic configuration of image parameters. Through functional simulation and off-board test, it is learned that, for the speed-insensitive path, the optimized algorithm can realize the complex operations of the original algorithm with less hardware resources; for hyperspectral image data with an effective input bit width of 12bit, the proposed method can reach a maximum operating frequency of 103MHz, and the data throughput of 103M samples per second (1.237Gbps).

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An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images

Cited by 16 publications

References 42 publications

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

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

Editorial to Special Issue “Remote Sensing Data Compression”

Design and Implementation of a Lossless Compression System for Hyperspectral Images

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