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Satellite Image Compression Guided by Regions of Interest

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispe...

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Detalles Bibliográficos
Autores principales: Schwartz, Christofer, Sander, Ingo, Bruhn, Fredrik, Persson, Mathias, Ekblad, Joakim, Fuglesang, Christer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861944/
https://www.ncbi.nlm.nih.gov/pubmed/36679527
http://dx.doi.org/10.3390/s23020730
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author Schwartz, Christofer
Sander, Ingo
Bruhn, Fredrik
Persson, Mathias
Ekblad, Joakim
Fuglesang, Christer
author_facet Schwartz, Christofer
Sander, Ingo
Bruhn, Fredrik
Persson, Mathias
Ekblad, Joakim
Fuglesang, Christer
author_sort Schwartz, Christofer
collection PubMed
description Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform’s constraints regarding processing time were analyzed by running the detection algorithms on Unibap’s iX10-100 space cloud platform.
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spelling pubmed-98619442023-01-22 Satellite Image Compression Guided by Regions of Interest Schwartz, Christofer Sander, Ingo Bruhn, Fredrik Persson, Mathias Ekblad, Joakim Fuglesang, Christer Sensors (Basel) Article Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform’s constraints regarding processing time were analyzed by running the detection algorithms on Unibap’s iX10-100 space cloud platform. MDPI 2023-01-09 /pmc/articles/PMC9861944/ /pubmed/36679527 http://dx.doi.org/10.3390/s23020730 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Schwartz, Christofer
Sander, Ingo
Bruhn, Fredrik
Persson, Mathias
Ekblad, Joakim
Fuglesang, Christer
Satellite Image Compression Guided by Regions of Interest
title Satellite Image Compression Guided by Regions of Interest
title_full Satellite Image Compression Guided by Regions of Interest
title_fullStr Satellite Image Compression Guided by Regions of Interest
title_full_unstemmed Satellite Image Compression Guided by Regions of Interest
title_short Satellite Image Compression Guided by Regions of Interest
title_sort satellite image compression guided by regions of interest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861944/
https://www.ncbi.nlm.nih.gov/pubmed/36679527
http://dx.doi.org/10.3390/s23020730
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