Abstract
The current inversion method of maize leaf area index has the problems of long time-consuming inversion, high energy consumption, and low fitting coefficient between the prediction result and the actual result. For these problems, an inversion method of maize leaf area index based on UAV hyperspectral remote sensing is proposed in this paper. Acquisition of spectral image of maize leaf using ASD Field SpecPro FR Field Hyperspectral instrument and CCD Array detector in UAV remote Sensing system. Denoising and segmentation of corn leaf images using stationary wavelets, improved Snake and PSO methods.. The improved Snake model is used to achieve coarse convergence of target image contour after denoising. Through particle swarm optimization iterative algorithm, the optimal image segmentation point is found and the image segmentation is achieved. Based on the results of image denoising segmentation, the expressions of modified chlorophyll absorption ratio, normalized difference spectral index, and ratio-type spectral index are obtained. The correlation between the three indices and the maize leaf area index was analyzed. Finally, the maize leaf area index was obtained by using ratio-type spectral index. Experiments show that the proposed method has a comprehensive performance, and has a strong advantage over the current method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Deng YB, Yu KY, Yu XY et al (2017) Estimation of individual tree leaf area index of terrestrial remote sensing inversion based on point cloud voxelization. J Northwest Forest Univ 32(5):191–197
Gaocheng L, Shuai L, Khan M et al (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6(1):29283–29296
Garreta V, Miller PA, Guiot J et al (2017) A method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model. Clim Dyn 35(1):371–389
Kubo H, Asano K, Iwata T et al (2016) Development of fully Bayesian multiple-time-window source inversion. Geophys J Int 204(6):1601–1619
Li CN, Zhang XF, Yu ZH et al (2016) Accuracy evaluation of summer maize coverage and leaf area index inversion based on images extraction technology. Chin J Agrometeorol 37(6):479–491
Liu S, Zhang Z, Qi L et al (2016) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tools Appl 75(23):15525–15536
Liu S, Fu W, He L et al (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802
Mallet A (2018) Numerical inversion of Laplace transform. Electron Lett 5:120–121
Mustać M, Tkalčić H (2016) Point source moment tensor inversion through a Bayesian hierarchical model. Geophys J Int 204(5):311–323
Nie S, Wang C, Dong P et al (2016) Estimating leaf area index of maize using airborne discrete-return LiDAR data. Remote Sens Lett 25(9):3259–3266
Olm JM, Ros-Oton X (2017) Approximate tracking of periodic references in a class of bilinear systems via stable inversion. Discrete Cont Dyn-B 15(4):197–215
Peng YF, Zhou J, Zi LL (2017) The simulation research on regional satellite remote sensing image segmentation method. Comput Simul 34(7):332–335
Piessens R, Branders M (2017) Numerical inversion of the Laplace transform using generalised Laguerre polynomials. Environ Prog Sustain Energy 36(4):1517–1522
Shuai L, Weiling B, Gaocheng L et al (2018) Parallel fractal compression method for big video data. Complexity 2018:2016976
Shuai L, Gaocheng L, Huiyu Z (2019) A robust parallel object tracking method for illumination variations. Moblie Netw Appl. https://doi.org/10.1007/s11036-018-1134-8
Sun H, Luo CQ, Lin H et al (2016) Study on remote sensing estimation of leaf area index based on k-NN algorithm. J Cent South Univ Forest Technol 36:11–17
Wang Q, Pang Y, Li ZY et al (2016) Inversion of leaf area index based on a simple physical model. J China Univ Min Technol 45(1):623–629
Wang HB, Zhao ZQ, Lin Y et al (2017) Leaf area index estimation of spring maize with canopy hyperspectral data based on linear regression algorithm. Spectrosc Spectr Anal 37(2):1489–1496
Wei X, Wang X, Guo S et al (2016) Epistatic and QTL×environment interaction effects on leaf area-associated traits in maize. Plant Breed 135(8):671–676
Yoshida N, Sagawa H, Otsuka T (2016) Signature inversion in odd-odd nuclei in the interacting boson-fermion model. Nucl Phys A 570(2):17–32
Zheng P, Shuai L, Arun S et al (2018) Visual attention feature (VAF): a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J Parallel Distr Com 120:182–194
Acknowledgments
This work is supported by foundation of science and technology department of Sichuan Province with No. 2017JZ0032.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, YM., Jiang, MJ. Comparison of inversion method of maize leaf area index based on UAV hyperspectral remote sensing. Multimed Tools Appl 79, 16385–16401 (2020). https://doi.org/10.1007/s11042-019-7318-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7318-9