In this work, we propose a new semi-supervised algorithm for remotely sensed hyperspectral image classification which belongs to the family of multiple classifier systems. The proposed approach combines the output of two well-established discriminative classifiers: sparse multinomial logistic regression (SMLR) and quadratic discriminant analysis (QDA). Our approach follows a two-step strategy. ...

This study aims to integrate multisource data model the relative soil moisture (RSM) over Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) order improve spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from Moderate Resolution Imaging Spectroradiometer (MODIS) topographic, properties, meteorological w...

Wheat yield production is largely attributed by weather parameters. Model developed multiple linear, neural network and penalised regression techniques using data have the potential to provide reliable, timely cost-effective prediction of wheat yield. parameter during crop growing period (46th 15th SMW) for more than 30 years were collected study area model was stepwise linear (SMLR), principal...

Formulated as a least square problem under an constraint, sparse signal restoration is a discrete optimization problem, known to be NP complete. Classical algorithms include, by increasing cost and efficiency, matching pursuit (MP), orthogonal matching pursuit (OMP), orthogonal least squares (OLS), stepwise regression algorithms and the exhaustive search. We revisit the single most likely repla...

In seeking to find diagnostic biomarkers in proteomic spectra, two significant problems arise. First, not only is there noise in the measured intensity at each m/z value, but there is also noise in the measured m/z value itself. Second, the potential for overfitting is severe: it is easy to find features in the spectra that accurately discriminate disease states but have no biological meaning. ...

In this correspondence, we propose a fast maximum likelihood detection and estimation algorithm, called a multiple-mostlikely-replacement (MMLR) detector, for Bernoulli-Gaussian processes which are distorted by a linear time-invariant system and contaminated by a white Gaussian noise. This new detector works as well as the well-known single-most-likely-replacement (SMLR) detector. However, the ...

The spectral reflectance technique for the quantification of functional components was applied in different studies crops, but related research on kale leaves is limited. This study conducted to estimate glucosinolate and anthocyanin cultivated a plant factory based diffuse spectroscopy through regression methods. Kale grown under treatments. After specific periods transplantation, leaf samples...