Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models
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| Publicado no: | Remote Sensing vol. 17, no. 12 (2025), p. 2001-2022 |
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| Autor principal: | |
| Outros Autores: | , , , , , , , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3223940085 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17122001 |2 doi | |
| 035 | |a 3223940085 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Yang, Weiguang |u College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; wgyang@scau.edu.cn (W.Y.); wjh@stu.scau.edu.cn (J.W.); ylan@scau.edu.cn (Y.L.) | |
| 245 | 1 | |a Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R2 values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. | |
| 651 | 4 | |a United Kingdom--UK | |
| 651 | 4 | |a Shenzhen China | |
| 651 | 4 | |a China | |
| 653 | |a Reflectance | ||
| 653 | |a Software | ||
| 653 | |a Models | ||
| 653 | |a Sensors | ||
| 653 | |a Calibration | ||
| 653 | |a Cameras | ||
| 653 | |a Remote sensing | ||
| 653 | |a Agriculture | ||
| 653 | |a Decision trees | ||
| 653 | |a Data conversion | ||
| 653 | |a Accuracy | ||
| 653 | |a Vegetation | ||
| 653 | |a Wind | ||
| 653 | |a Vegetation index | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Radiometric correction | ||
| 653 | |a Cost analysis | ||
| 653 | |a Multisensor applications | ||
| 653 | |a Climate | ||
| 700 | 1 | |a Fu Huaiyuan |u College of Agriculture, South China Agricultural University, Guangzhou 510642, China; hyfu@stu.scau.edu.cn (H.F.); lsy@stu.scau.edu.cn (S.L.); xili@stu.scau.edu.cn (X.L.); jttan@stu.scau.edu.cn (J.T.) | |
| 700 | 1 | |a Xu Weicheng |u Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510642, China; xwc@gdaas.cn | |
| 700 | 1 | |a Wu Jinhao |u College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; wgyang@scau.edu.cn (W.Y.); wjh@stu.scau.edu.cn (J.W.); ylan@scau.edu.cn (Y.L.) | |
| 700 | 1 | |a Liu, Shiyuan |u College of Agriculture, South China Agricultural University, Guangzhou 510642, China; hyfu@stu.scau.edu.cn (H.F.); lsy@stu.scau.edu.cn (S.L.); xili@stu.scau.edu.cn (X.L.); jttan@stu.scau.edu.cn (J.T.) | |
| 700 | 1 | |a Li, Xi |u College of Agriculture, South China Agricultural University, Guangzhou 510642, China; hyfu@stu.scau.edu.cn (H.F.); lsy@stu.scau.edu.cn (S.L.); xili@stu.scau.edu.cn (X.L.); jttan@stu.scau.edu.cn (J.T.) | |
| 700 | 1 | |a Tan Jiangtao |u College of Agriculture, South China Agricultural University, Guangzhou 510642, China; hyfu@stu.scau.edu.cn (H.F.); lsy@stu.scau.edu.cn (S.L.); xili@stu.scau.edu.cn (X.L.); jttan@stu.scau.edu.cn (J.T.) | |
| 700 | 1 | |a Lan Yubin |u College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China; wgyang@scau.edu.cn (W.Y.); wjh@stu.scau.edu.cn (J.W.); ylan@scau.edu.cn (Y.L.) | |
| 700 | 1 | |a Zhang, Lei |u College of Agriculture, South China Agricultural University, Guangzhou 510642, China; hyfu@stu.scau.edu.cn (H.F.); lsy@stu.scau.edu.cn (S.L.); xili@stu.scau.edu.cn (X.L.); jttan@stu.scau.edu.cn (J.T.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 12 (2025), p. 2001-2022 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223940085/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223940085/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223940085/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |