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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/730 |
| _version_ | 1797437331477626880 |
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| author | Christofer Schwartz Ingo Sander Fredrik Bruhn Mathias Persson Joakim Ekblad Christer Fuglesang |
| author_facet | Christofer Schwartz Ingo Sander Fredrik Bruhn Mathias Persson Joakim Ekblad Christer Fuglesang |
| author_sort | Christofer Schwartz |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T11:18:39Z |
| format | Article |
| id | doaj.art-dea9d8fe971941eba05977dbc91b4d51 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:18:39Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dea9d8fe971941eba05977dbc91b4d512023-12-01T00:26:36ZengMDPI AGSensors1424-82202023-01-0123273010.3390/s23020730Satellite Image Compression Guided by Regions of InterestChristofer Schwartz0Ingo Sander1Fredrik Bruhn2Mathias Persson3Joakim Ekblad4Christer Fuglesang5KTH Royal Institute of Technology, 100 44 Stockholm, SwedenKTH Royal Institute of Technology, 100 44 Stockholm, SwedenUnibap AB, Kungsängsgatan 12, 753 22 Uppsala, SwedenUnibap AB, Kungsängsgatan 12, 753 22 Uppsala, SwedenSaab AB, Olof Palmes Gata 17, 111 22 Stockholm, SwedenKTH Royal Institute of Technology, 100 44 Stockholm, SwedenSmall 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.https://www.mdpi.com/1424-8220/23/2/730satellite communicationimage compressioncloud detectionvessel detectionchange detection |
| spellingShingle | Christofer Schwartz Ingo Sander Fredrik Bruhn Mathias Persson Joakim Ekblad Christer Fuglesang Satellite Image Compression Guided by Regions of Interest Sensors satellite communication image compression cloud detection vessel detection change detection |
| 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 | satellite communication image compression cloud detection vessel detection change detection |
| url | https://www.mdpi.com/1424-8220/23/2/730 |
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