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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121912 |
