Optimal combination of the correction model and parameters for the precision geometric correction of UAV hyperspectral images
Keywords:
geometric correction, hyperspectral images, unmanned aerial vehicle (UAV), ground control point (GCP)Abstract
Nowadays, with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology, people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images. The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images. In this study, the geometric correction accuracy under various geometric correction models (including affine transformation model, local triangulation model, polynomial model, direct linear transformation model, and rational function model) and resampling methods (including nearest neighbor resampling method, bilinear interpolation resampling method, and cubic convolution resampling method) were analyzed. Furthermore, the distribution, number, and accuracy of control points were analyzed based on the control variable method, and precise ground control points (GCPs) were analyzed. The results showed that the average geometric positioning error of UAV hyperspectral images (at 80 m altitude AGL) without geometric correction was as high as 3.4041 m (about 65 pixels). The optimal geometric correction model and parameter combination of the UAV hyperspectral image (at 80 m altitude AGL) used a local triangulation model, adopted a bilinear interpolation resampling method, and selected 12 edge-middle distributed GCPs. The correction accuracy could reach 0.0493 m (less than one pixel). This study provides a reference for the geometric correction of UAV hyperspectral images. Keywords: geometric correction, hyperspectral images, unmanned aerial vehicle (UAV), ground control point (GCP) DOI: 10.25165/j.ijabe.20241703.7103 Citation: Tian W Z, Kan Z, Zhao Q Z, Jiang P, Wang X W, Liu H Q. Optimal combination of the correction model and parameters for the precision geometric correction of UAV hyperspectral images. Int J Agric & Biol Eng, 2024; 17(3): 173-184.References
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[41] Li A, Huang F R, Wen Z. Plane accuracy analysis of multi-source high resolution satellite image. In: 2019 IEEE International Conference on Signal, Information and Data Processing, Chongqing: IEEE, 2019; pp.1–6.
[2] Liu X M, Wang H C, Cao Y W, Yang Y T, Sun X T, Sun K, et al. Comprehensive growth index monitoring of desert stepp.grassland vegetation based on UAV hyperspectral. Frontiers in Plant Science, 2023; 13: 1050999.
[3] Banerjee B P, Raval S, Cullen P J. UAV-hyperspectral imaging of spectrally complex environments. International Journal of Remote Sensing, 2020; 41(11): 4136–4159.
[4] Aasen H, Honkavaara E, Lucieer A, Zarco-Tejada P J. Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sensing, 2018; 10(7): 1091.
[5] Senn J A, Mills J P, Miller P E, Walsh C, Addy S, Loerke E, et al. On-site geometric calibration of thermal and optical sensors for UAS photogrammetry. Remote Sensing and Spatial Information Sciences, 2020; XLIII(B1): 355–361.
[6] Song L Y, Li H W, Chen T Q, Chen J Y, Liu S, Fan J, et al. An integrated solution of UAV push-broom hyperspectral system based on geometric correction with MSI and radiation correction considering outdoor illumination variation. Remote Sensing, 2022; 14(24): 6267.
[7] Wang T Y, Zhang G, Yu L, Zhao R S, Deng M J, Xu K. Multi-mode GF-3 satellite image geometric accuracy verification using the RPC model. Sensors, 2017; 17(9): 2005.
[8] Ma S B, Yang W F, Pi Y N, Li S H, Xin R F. Research on geometrical calibration precision of GF-2 satellite data based on RFM model. Techniques of Automation and Applications, 2020; 39(9): 57–60. (in Chinese)
[9] Zhong X M. Research on geometric rectification of high resolution remote sensing image in forest areas. Master dissertation. Xi’an: Xi’an University of Science and Technology, 2011; pp.44–51. (in Chinese)
[10] Wang L. Research on the methods of GF-2 satellite remote sensing image geometric correction. Master dissertation. Changchun: Jilin University, 2018; pp.33–41. (in Chinese)
[11] Wang Z W, Yang G D, Zhang X Q, Wang F Y, Bi Y H. A comparative research on the accuracy of different geometric correction methods of Gaofen-6 satellite remote sensing image. World Geology, 2021; 40(1): 125–130, 139. (in Chinese)
[12] Eltohamy F, Hamza E H. Effect of ground control points location and distribution on geometric correction accuracy of remote sensing satellite images. In: 13th International Conference on Aerospace Sciences & Aviation Technology (ASAT-13), Vienna, Austria, 2009; pp.1–14.
[13] Wang J H, Ge Y, Heuvelink G B M, Zhou C H, Brus D. Effect of the sampling design of ground control points on the geometric correction of remotely sensed imagery. International Journal of Applied Earth Observation and Geoinformation, 2012; 18: 91–100.
[14] Babiker M E A, Akhadir S K Y. The effect of densification and distribution of control points in the accuracy of geometric correction. International Journal of Scientific Research in Science, Engineering and Technology, 2016; 2(1): 65–70.
[15] Guo C. Research on geometric correction method of Jilin-1 satellite image. Master dissertation. Jilin: Jilin University, 2019; pp.35–45. (in Chinese)
[16] Jiang C S, Zhang X Q, Yang G D, Geng Y D, Liu Z W, Wang T. Geometric correction method test of Jilin 1 satellite image. Geomatics & Spatial Information Technology, 2020; 43(8): 208–211. (in Chinese)
[17] Yang G P. Research and application on geometric correction methods of remote sensing image. Master dissertation. Xi’an: Xi’an University of Architecture and Technology, 2010; pp.16–19. (in Chinese)
[18] Hruska R, Mitchell J, Anderson M, Glenn N F. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sensing, 2012; 4(9): 2736–2752.
[19] Tawfik M, Elhifnawy H, Ragab A, Hamza E. The effect of image resolution on the geometric correction of remote sensing satellite images. International Journal of Engineering and Applied Sciences, 2017; 4(5): 114–121.
[20] Jiang Y, Li N, Meng L J, Cai H, Gong X M, Zhao H J. Geometric correction method of core hyperspectral data based on error analysis. Infrared and Laser Engineering, 2018; 47(5): 175–182. (in Chinese)
[21] Li C R. UAV remote sensing load comprehensive verification system technology, 1st ed. Beijing: China Science Publishing & Media Ltd., 2014; pp.164–186.
[22] Mesas-Carrascosa F J, Torres-Sánchez J, Clavero-Rumbao I, García-Ferrer A, Peña J M, Borra-Serrano I, et al. Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by UAV to support site-specific crop management. Remote Sensing, 2015; 7: 12793–12814.
[23] Saillen N, Aballea L, Buhler D, Montrone L, Reyes D N, Francois M, et al. Technological innovation for the ALTIUS atmospheric limb sounding mission: steps towards flight. International Conference on Space Optics - ICSO 2022, Dubrovnik: SPIE 2023; 12777: 210–229.
[24] Tian W Z, Zhao Q Z, Hu H W, Ma Y J, Li P T, Luo X Z. A movable target for ground resolution evaluation of UAV optical load. CN208833469U. 2019.
[25] Zhao X Q, Yang G J, Liu J G, Zhang X Y, Xu B, Wang Y J, et al. Estimation of soybean breeding yield based on optimization of spatial scale of UAV hyperspectral image. Transactions of the CSAE, 2017; 33(1): 110–116. (in Chinese)
[26] Geipel J, Bakken A K, Jørgensen M, Korsaeth A. Forage yield and quality estimation by means of UAV and hyperspectral imaging. Precision Agriculture, 2021; 22: 1437–1463.
[27] Yu R, Luo Y Q, Zhou Q, Zhang X D, Wu D W, Ren L L. A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level. International Journal of Applied Earth Observation and Geoinformation, 2021; 101(1): 102363.
[28] Tian W Z, Zhao Q Z, Ma Y J, Long X, Wang X W. Flight parameter setting of unmanned aerial vehicle hyperspectral load. Journal of Applied Spectroscopy, 2022; 89(1): 159–169.
[29] Kern J, Schenk A, Hinz S. Radiometric calibration of a UAV-mounted hyperspectral snapshot camera with focus on uniform spectral sampling. In: 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2018; pp.1–5.
[30] Cao S, Danielson B, Clare S, Koenig S, Campos-Vargas C, Sanchez-Azofeifa A. Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols. ISPRS Journal of Photogrammetry and Remote Sensing, 2019; 149: 132–145.
[31] Poncet A M, Knappenberger T, Brodbeck C, Fogle Jr. M, Shaw J N, Oriz B V. Multispectral UAS data accuracy for different radiometric calibration methods. Remote Sensing, 2019; 11(16): 1917.
[32] Storey J C, Rengarajan R, Choate M J. Bundle adjustment using space-based triangulation method for improving the Landsat global ground reference. Remote Sensing, 2019; 11(14): 1640.
[33] Gholinejada S, Amiri-Simkooei A, Moghaddam S H A, Naeini A A. An automated PCA-based approach towards optimization of the rational function model. ISPRS Journal of Photogrammetry and Remote Sensing, 2020; 165: 133–139.
[34] Inamdar D, Kalacska M, Darko P O, Arroyo-Mora J P, Leblanc G. Spatial response resampling (SR2): Accounting for the spatial point spread function in hyperspectral image resampling. MethodsX, 2023; 10: 101998.
[35] Lyons M B, Keith D A, Phinn S R, Mason T J, Elith J. A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sensing of Environment, 2018; 208: 145–153.
[36] Wang X X, Zuo X Q, Yang Z N. Research on realization and application of remote sensing image resampling. Computer Engineering & Software, 2019; 40(7): 42–46. (in Chinese)
[37] Tawfeik M, Elhifnawy H, Hamza E, Shawky A. Determination of suitable requirements for geometric correction of remote sensing satellite images when using ground control points. International Research Journal of Engineering and Technology, 2016; 3(10): 54–62.
[38] CH/Z 3003-2010. Specifications for office operation of low-altitude digital aerophotogrammetry. 2010.
[39] ISO 12233-2000. Photography-electronic still-picture cameras-resolution measurements. 2000.
[40] Wang Y H, Cong Q, Yao S, Jia X Y, Chen J Y, Li S Y. Research on geometric error correction of pushbroom hyperspectral camera carried by UAV. Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, Kunming, China, 2021; 117634G.
[41] Li A, Huang F R, Wen Z. Plane accuracy analysis of multi-source high resolution satellite image. In: 2019 IEEE International Conference on Signal, Information and Data Processing, Chongqing: IEEE, 2019; pp.1–6.
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Published
2024-07-11
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Tian, W., Kan, Z., Zhao, Q., Jiang, P., Wang, X., & Liu, H. (2024). Optimal combination of the correction model and parameters for the precision geometric correction of UAV hyperspectral images. International Journal of Agricultural and Biological Engineering, 17(3), 173–184. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7103
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Information Technology, Sensors and Control Systems
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