research-article

An SVM-based hardware accelerator for onboard classification of hyperspectral images

Authors:

Lucas A. Martins,

Guilherme A. M. Sborz,

Cesar A. ZeferinoAuthors Info & Claims

SBCCI '19: Proceedings of the 32nd Symposium on Integrated Circuits and Systems Design

Article No.: 18, Pages 1 - 6

https://doi.org/10.1145/3338852.3339869

Published: 26 August 2019 Publication History

Abstract

Hyperspectral images (HSIs) have been used in civil and military scenarios for ground recognition, urban development management, rare minerals identification, and diverse other purposes. However, HSIs have a significant volume of information and require high computational power, especially for real-time processing in embedded applications, as in onboard computers in satellites. These issues have driven the development of hardware-based solutions able to provide the processing power necessary to meet such requirements. In this paper, we present a hardware accelerator to enhance the performance of one of the most computational expensive stages of HSI processing: the classification. We have employed the Entropy Multiple Correlation Ratio procedure to select the spectral bands to be used in the training process. For the classification step, we have applied a Support Vector Machine classifier with a Hamming Distance decision approach. The proposed custom processor was implemented in FPGA and compared with high-level implementations. The results obtained demonstrate that the processor has a silicon cost lower than similar solutions and can perform a real-time pixel classification in 0.1 ms and achieves a state-of-the-art accuracy of 99.7%.

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Cited By

Martins LViel FSeman LBezerra EZeferino C(2024)A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGAMicroprocessors and Microsystems10.1016/j.micpro.2023.104998104(104998)Online publication date: Feb-2024
https://doi.org/10.1016/j.micpro.2023.104998
Yuan Shuai 袁Sun Yanan 孙He Weifeng 何Tu Shikui 涂(2023)基于多尺度特征提取的高光谱星载分类算法Laser & Optoelectronics Progress10.3788/LOP21328960:10(1010004)Online publication date: 2023
https://doi.org/10.3788/LOP213289
Viel FMaciel RSeman LZeferino CBezerra ELeithardt V(2023)Hyperspectral Image Classification: An Analysis Employing CNN, LSTM, Transformer, and Attention MechanismIEEE Access10.1109/ACCESS.2023.325516411(24835-24850)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3255164
Show More Cited By

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An SVM-based hardware accelerator for onboard classification of hyperspectral images

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Published In

cover image ACM Conferences

SBCCI '19: Proceedings of the 32nd Symposium on Integrated Circuits and Systems Design

August 2019

204 pages

ISBN:9781450368445

DOI:10.1145/3338852

General Chair:
João Martino
USP, Brazil
,
Program Chairs:
Marcelo Lubaszewski
UFRGS, Brazil
,
Matteo Reorda
Politec. di Torino, Italy

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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IEEE Circuits & Systems Society

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New York, NY, United States

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Published: 26 August 2019

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Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina
Conselho Nacional de Desenvolvimento Científico e Tecnológico

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SBCCI '19

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SIGDA

SBCCI '19: 32nd Symposium on Integrated Circuits and Systems Design

August 26 - 30, 2019

São Paulo, Brazil

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Overall Acceptance Rate 133 of 347 submissions, 38%

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Cited By

Martins LViel FSeman LBezerra EZeferino C(2024)A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGAMicroprocessors and Microsystems10.1016/j.micpro.2023.104998104(104998)Online publication date: Feb-2024
https://doi.org/10.1016/j.micpro.2023.104998
Yuan Shuai 袁Sun Yanan 孙He Weifeng 何Tu Shikui 涂(2023)基于多尺度特征提取的高光谱星载分类算法Laser & Optoelectronics Progress10.3788/LOP21328960:10(1010004)Online publication date: 2023
https://doi.org/10.3788/LOP213289
Viel FMaciel RSeman LZeferino CBezerra ELeithardt V(2023)Hyperspectral Image Classification: An Analysis Employing CNN, LSTM, Transformer, and Attention MechanismIEEE Access10.1109/ACCESS.2023.325516411(24835-24850)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3255164
Sengupta AChaurasia RRathor M(2023)HLS‐based swarm intelligence driven optimized hardware IP core for linear regression‐based machine learningThe Journal of Engineering10.1049/tje2.122992023:8Online publication date: 14-Aug-2023
https://doi.org/10.1049/tje2.12299
Haidar JMustapha SSalam DTehrani A(2022)Optimal Spatial-Spectral Input For Real-Time Hyperspectral Image Classification2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)10.1109/WHISPERS56178.2022.9955075(1-5)Online publication date: 13-Sep-2022
https://doi.org/10.1109/WHISPERS56178.2022.9955075
Nair ANath PChakrabartty SThakur C(2022)Multiplierless MP-Kernel Machine for Energy-Efficient Edge DevicesIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.318978030:11(1601-1614)Online publication date: Nov-2022
https://doi.org/10.1109/TVLSI.2022.3189780

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