Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

Farifteh, Jamshid; Meer, Freek D. van der; Atzberger, Clement; Carranza, Emmanuel John M.

doi:10.1016/j.rse.2007.02.005

Cited by 370 publications

(239 citation statements)

References 28 publications

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“…Farifteh et al [92] indicated that PLSR performs more stably in soil salinity estimation than ANN (PLSR: R 2 : 0.6~0.98, RMSE% = 11.6~48%; ANN: R 2 = 0.46~0.97, RMSE% = 12.5~57%). Thus, PLSR and MLR may be suitable for works in which fewer samples are available for modeling.…”

Section: Analysis Of Stability and Prediction Performancementioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

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Section: Analysis Of Stability and Prediction Performancementioning

confidence: 99%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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“…Indeed, its goal is to predict the dependent variables (Y), selecting a limited number of new orthogonal factors (T) and a set of specific loadings (P) able to simultaneously both X and Y, in order to reduce the high dimensionality of the input dataset [8,22,33,36,[61][62][63][64]. Thus, X is calculated by applying the following equation:…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

121

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Grassland ecosystems cover around 40% of the entire Earth's surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Rededge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots.

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“…The general idea of PLSR is to extract the orthogonal or latent predictor variables that will account for as much of the variation of the dependent variable (s) as possible (Farifteh et al, 2007). A detailed description of the PLSR technique can be found in (Wold et al, 2001).…”

Section: Discrimination Modelsmentioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

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This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400 _ 1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.Keywords: hyper-spectral imaging, waxed chestnut, effective wavelength, pear IntroductionThe chestnut (Castanea mollissima Bl.) is an important edible fruit in the northern hemisphere, which has been consumed as extensively as the potato in the past (Ferreira-Cardoso et al., 1999).and has become increasingly important in human nutrition because of its nutrient composition and potential beneficial health effects, for example, in reducing coronary heart disease and cancer rates (Sabaté et al., 2001). Fried chestnut is the most popular preparation method for chestnuts. However, some unscrupulous traders add industrial wax while frying chestnuts to ensure that the chestnuts are brighter and more attractive. Generally, industrial wax is extracted directly from oil, which will also extract polycyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons which are present in the industrial process, both of which are highly carcinogenic (Bonser et al., 1963). Mixed industrial wax will infiltrate into the pulp in the process of frying chestnuts which seriously endangers human health. Hence, a sorting process is necessary to identify and screen out waxed chestnuts.Traditionally, screening waxed chestnuts is usually dependent on subjective criteria to distinguish between waxed and non-waxed chestnuts, taking into consideration that brighter and more attractive chestnuts are mixed with industrial wax. Although industrial wax will make chestnuts brighter and more attractive, it is not a reliable way to identify waxed chestnuts. Hyper-spectral imaging (also called imaging spectrometry or imaging spectroscopy) is an emerging analytical technology which is receiving growing attention as a rapid, efficient, and nondestructive analytical tool for quantitative determination of the quality and safety of agricultural goods. By combining the advantages of computer vision and spectroscopy technology into one system, hyper-spectral imaging can generate a spatial image of the spectral variation, which provides structural, chemical, physical and functional information abou...

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Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

Cited by 370 publications

References 28 publications

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

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