Embedded Compression Algorithm for Agricultural Optical Remote Sensing Images Based on Adaptive Sparse Coding.
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| Title: | Embedded Compression Algorithm for Agricultural Optical Remote Sensing Images Based on Adaptive Sparse Coding. |
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| Authors: | Zhao, Rongqiang1,2,3 (AUTHOR), Huang, Zhennan1,2 (AUTHOR), Yin, Tiangang3,4 (AUTHOR), Meng, Ran1,2,4 (AUTHOR) mengran@hit.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1912. 18p. |
| Subjects: | Agricultural remote sensing, Image compression, High resolution imaging, Precision farming, Digital communications, Compressed sensing, Edge computing, Aerial surveillance |
| Abstract: | Highlights: What are the main findings? A content-aware adaptive sparse codingmethodwas developed and successfully deployed on a 5G-integrated edge computing node for efficient agricultural image compression. The system reduces 5G data transmission windows by over 90% at a 95% compression ratio, while maintaining the deviation of critical agronomic indices (NDVI, NDRE, and GNDVI) within 5%. What are the implications of the main finding? The framework effectively resolves bandwidth bottlenecks and latency issues encountered when transmitting massive high-resolution remote sensing data from field equipment like UAVs. The proposed hardware–software co-design shifts agricultural image processing from an offline paradigm to real-time online acquisition, supporting timely crop monitoring and precision farming decisions. High-resolution remote sensing is essential for information acquisition in smart agriculture, yet real-time processing remains a critical challenge. Although high-resolution imagery provides comprehensive data, its massive volume complicates efficient handling. Existing techniques are predominantly restricted to offline scenarios, conflicting with the practical requirements for online acquisition and transmission. To address these challenges, we propose an adaptive sparse coding method for agricultural remote sensing images that dynamically selects compression strategies based on image content. Using this approach, we developed an embedded terminal system for real-time agricultural data transmission over 5G networks. Experimental results show that at a 95% compression ratio, transmission time is reduced by over 90% compared with uncompressed images. The method also achieves high-fidelity reconstruction; the deviation rates for the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) remain below 5% even at a 97% compression ratio. This approach offers fast transmission, high compression efficiency, and strong reconstruction quality, making it suitable for field equipment such as unmanned aerial vehicles in real-time monitoring networks. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915045 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Embedded Compression Algorithm for Agricultural Optical Remote Sensing Images Based on Adaptive Sparse Coding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Rongqiang%22">Zhao, Rongqiang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Zhennan%22">Huang, Zhennan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Tiangang%22">Yin, Tiangang</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Ran%22">Meng, Ran</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<i> mengran@hit.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1912. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Agricultural+remote+sensing%22">Agricultural remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+communications%22">Digital communications</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+surveillance%22">Aerial surveillance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A content-aware adaptive sparse codingmethodwas developed and successfully deployed on a 5G-integrated edge computing node for efficient agricultural image compression. The system reduces 5G data transmission windows by over 90% at a 95% compression ratio, while maintaining the deviation of critical agronomic indices (NDVI, NDRE, and GNDVI) within 5%. What are the implications of the main finding? The framework effectively resolves bandwidth bottlenecks and latency issues encountered when transmitting massive high-resolution remote sensing data from field equipment like UAVs. The proposed hardware–software co-design shifts agricultural image processing from an offline paradigm to real-time online acquisition, supporting timely crop monitoring and precision farming decisions. High-resolution remote sensing is essential for information acquisition in smart agriculture, yet real-time processing remains a critical challenge. Although high-resolution imagery provides comprehensive data, its massive volume complicates efficient handling. Existing techniques are predominantly restricted to offline scenarios, conflicting with the practical requirements for online acquisition and transmission. To address these challenges, we propose an adaptive sparse coding method for agricultural remote sensing images that dynamically selects compression strategies based on image content. Using this approach, we developed an embedded terminal system for real-time agricultural data transmission over 5G networks. Experimental results show that at a 95% compression ratio, transmission time is reduced by over 90% compared with uncompressed images. The method also achieves high-fidelity reconstruction; the deviation rates for the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) remain below 5% even at a 97% compression ratio. This approach offers fast transmission, high compression efficiency, and strong reconstruction quality, making it suitable for field equipment such as unmanned aerial vehicles in real-time monitoring networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18121912 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1912 Subjects: – SubjectFull: Agricultural remote sensing Type: general – SubjectFull: Image compression Type: general – SubjectFull: High resolution imaging Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Digital communications Type: general – SubjectFull: Compressed sensing Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Aerial surveillance Type: general Titles: – TitleFull: Embedded Compression Algorithm for Agricultural Optical Remote Sensing Images Based on Adaptive Sparse Coding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Rongqiang – PersonEntity: Name: NameFull: Huang, Zhennan – PersonEntity: Name: NameFull: Yin, Tiangang – PersonEntity: Name: NameFull: Meng, Ran IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
