A multiband model with successive projections algorithm for bathymetry estimation based on remotely sensed hyperspectral data in Qinghai Lake

Abstract Background In order to accurately estimate leaf area index (LAI) of winter wheat by using unmanned aerial vehicle (UAV) hyperspectral imagery.Methods The UAV hyperspectral imaging data, alternating slice-wise diagonalization (ASD) spectral data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments.The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.Results Our results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information.The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. Our results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

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Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

Plant Methods ◽

10.1186/s13007-021-00750-5 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Juanjuan Zhang ◽

Tao Cheng ◽

Wei Guo ◽

Xin Xu ◽

Hongbo Qiao ◽

...

Keyword(s):

Machine Learning ◽

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Hyperspectral Data ◽

Learning Methods ◽

Successive Projections Algorithm ◽

Area Index ◽

Machine Learning Methods ◽

Successive Projections

Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

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Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression ( i SPA-PLS)

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2017.08.046 ◽

2018 ◽

Vol 189 ◽

pp. 300-306 ◽

Cited By ~ 24

Author(s):

Gabriela Krepper ◽

Florencia Romeo ◽

David Douglas de Sousa Fernandes ◽

Paulo Henrique Gonçalves Dias Diniz ◽

Mário César Ugulino de Araújo ◽

...

Keyword(s):

Nir Spectroscopy ◽

Fat Content ◽

Pls Regression ◽

Successive Projections Algorithm ◽

Interval Selection ◽

Successive Projections

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Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm

Applied Soft Computing ◽

10.1016/j.asoc.2017.08.007 ◽

2018 ◽

Vol 70 ◽

pp. 1135-1145 ◽

Cited By ~ 7

Author(s):

Diego P.P. Mesquita ◽

João Paulo P. Gomes ◽

Leonardo R. Rodrigues ◽

Saulo A.F. Oliveira ◽

Roberto K.H. Galvão

Keyword(s):

Neural Networks ◽

Successive Projections Algorithm ◽

Successive Projections

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Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples

Journal of the Brazilian Chemical Society ◽

10.1590/s0103-50532010000400024 ◽

2010 ◽

Vol 21 (4) ◽

pp. 760-763 ◽

Cited By ~ 7

Author(s):

Anderson S. Soares ◽

Arlindo R. Galvão Filho ◽

Roberto K. H. Galvão ◽

Mário César U. Araújo

Keyword(s):

Computational Efficiency ◽

Spectrometric Analysis ◽

Successive Projections Algorithm ◽

Sequential Regression ◽

Successive Projections

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OpenCL-library-based implementation of SCLSU algorithm for remotely sensed hyperspectral data exploitation: clMAGMA versus viennaCL

10.1117/12.2241524 ◽

2016 ◽

Author(s):

Sergio Bernabé ◽

Guillermo Botella ◽

Carlos Orueta ◽

José M. R. Navarro ◽

Manuel Prieto-Matías ◽

...

Keyword(s):

Remotely Sensed ◽

Hyperspectral Data

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Mapping contaminated soils: using remotely-sensed hyperspectral data to predict pH

European Journal of Soil Science ◽

10.1111/ejss.12160 ◽

2014 ◽

Vol 65 (6) ◽

pp. 897-906 ◽

Cited By ~ 8

Author(s):

C. C. H. Ong ◽

T. J. Cudahy

Keyword(s):

Contaminated Soils ◽

Remotely Sensed ◽

Hyperspectral Data

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Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033519898890 ◽

2020 ◽

Vol 28 (2) ◽

pp. 70-80 ◽

Cited By ~ 1

Author(s):

Perez Mukasa ◽

Collins Wakholi ◽

Akbar Faqeerzada Mohammad ◽

Eunsoo Park ◽

Jayoung Lee ◽

...

Keyword(s):

Discriminant Analysis ◽

Hyperspectral Imaging ◽

Imaging Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Successive Projections Algorithm ◽

Hinoki Cypress ◽

Sorting System ◽

Shortwave Infrared ◽

Successive Projections

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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