Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices

Hyperspectral sensors capture a wide range of spectral data, making them crucial for Earth observation applications, but this fact poses significant challenges for embedded systems with limited resources. Nevertheless, most studies only perform one application at the same time, so multi-applications...

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Detalles Bibliográficos
Autores: Díaz , María, Mira Serrano, José Luis, Lopez , Sebastián, Caba Jiménez, Julián, Barba Romero, Jesús, López López, Juan Carlos
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/43611
Acceso en línea:https://doi.org/10.1007/s11554-025-01692-0
https://link.springer.com/article/10.1007/s11554-025-01692-0
https://hdl.handle.net/10578/43611
Access Level:acceso abierto
Palabra clave:Anomaly detection
Embedded systems
FPGA device
Hyperspectral sensors
Lossy compression
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spelling Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devicesDíaz , MaríaMira Serrano, José LuisLopez , SebastiánCaba Jiménez, JuliánBarba Romero, JesúsLópez López, Juan CarlosAnomaly detectionEmbedded systemsFPGA deviceHyperspectral sensorsLossy compressionHyperspectral sensors capture a wide range of spectral data, making them crucial for Earth observation applications, but this fact poses significant challenges for embedded systems with limited resources. Nevertheless, most studies only perform one application at the same time, so multi-applications in the same device are not considered since high-performance and low hardware resources are limited. In this sense, this paper presents a hardware-friendly algorithm for concurrently execution of anomaly detection and lossy compression for hyperspectral imaging. The proposed algorithm reuses a hardware platform to perform both tasks in parallel, offering a validated hardware architecture designed for deployment on a cost-optimized FPGA device. The experimental results show that our hardware component can process hyperspectral images with a resolution of 825x1024 pixels and 160 bands in 0.53 s (486 MB/s), with a power consumption of 1.08 watts (399 MB/W).Springer202520252025info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1007/s11554-025-01692-0https://link.springer.com/article/10.1007/s11554-025-01692-0https://hdl.handle.net/10578/43611reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/436112026-05-27T07:36:41Z
dc.title.none.fl_str_mv Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
title Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
spellingShingle Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
Díaz , María
Anomaly detection
Embedded systems
FPGA device
Hyperspectral sensors
Lossy compression
title_short Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
title_full Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
title_fullStr Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
title_full_unstemmed Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
title_sort Concurrent execution of lossy compression and anomaly detection of hyperspectral images on FPGA devices
dc.creator.none.fl_str_mv Díaz , María
Mira Serrano, José Luis
Lopez , Sebastián
Caba Jiménez, Julián
Barba Romero, Jesús
López López, Juan Carlos
author Díaz , María
author_facet Díaz , María
Mira Serrano, José Luis
Lopez , Sebastián
Caba Jiménez, Julián
Barba Romero, Jesús
López López, Juan Carlos
author_role author
author2 Mira Serrano, José Luis
Lopez , Sebastián
Caba Jiménez, Julián
Barba Romero, Jesús
López López, Juan Carlos
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Anomaly detection
Embedded systems
FPGA device
Hyperspectral sensors
Lossy compression
topic Anomaly detection
Embedded systems
FPGA device
Hyperspectral sensors
Lossy compression
description Hyperspectral sensors capture a wide range of spectral data, making them crucial for Earth observation applications, but this fact poses significant challenges for embedded systems with limited resources. Nevertheless, most studies only perform one application at the same time, so multi-applications in the same device are not considered since high-performance and low hardware resources are limited. In this sense, this paper presents a hardware-friendly algorithm for concurrently execution of anomaly detection and lossy compression for hyperspectral imaging. The proposed algorithm reuses a hardware platform to perform both tasks in parallel, offering a validated hardware architecture designed for deployment on a cost-optimized FPGA device. The experimental results show that our hardware component can process hyperspectral images with a resolution of 825x1024 pixels and 160 bands in 0.53 s (486 MB/s), with a power consumption of 1.08 watts (399 MB/W).
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s11554-025-01692-0
https://link.springer.com/article/10.1007/s11554-025-01692-0
https://hdl.handle.net/10578/43611
url https://doi.org/10.1007/s11554-025-01692-0
https://link.springer.com/article/10.1007/s11554-025-01692-0
https://hdl.handle.net/10578/43611
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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