scholarly journals Estimating chromium concentration in arable soil based on the optimal principal components by hyperspectral data

2021 ◽  
Vol 133 ◽  
pp. 108400
Author(s):  
Fei Guo ◽  
Zhen Xu ◽  
Honghong Ma ◽  
Xiujin Liu ◽  
Shiqi Tang ◽  
...  
2013 ◽  
Vol 6 (5) ◽  
pp. 8339-8370 ◽  
Author(s):  
A. Hollstein ◽  
R. Lindstrot

Abstract. Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top of atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.


2014 ◽  
Vol 7 (2) ◽  
pp. 599-607 ◽  
Author(s):  
A. Hollstein ◽  
R. Lindstrot

Abstract. Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top-of-atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.


2002 ◽  
Author(s):  
J. Scott Tyo ◽  
Athanasios Konsolakis ◽  
David I. Dierson ◽  
Richard C. Olsen

2016 ◽  
Vol 71 (3) ◽  
pp. 507-519 ◽  
Author(s):  
Stephen M. Anthony ◽  
Jerilyn A. Timlin

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.


Author(s):  
E. L. Hall

Sensitization in stainless steels is caused by the formation of chromium-rich M23C6 carbides at grain boundaries, which depletes the adjacent matrix and boundary region of chromium, and hence leads to rapid intergranular attack. To fully understand the sensitization process, and to test the accuracy of theories proposed to model this process, it is necessary to obtain very accurate measurements of the chromium concentration at grain boundaries in sensitized specimens. Quantitative X-ray spectroscopy in the analytical electron microscope (AEM) enables the chromium concentration profile across these boundaries to be studied directly; however, it has been shown that a strong effect of foil thickness and electron probe size may be present in the analysis of rapidly-changing compositional gradients. The goal of this work is to examine these effects.


2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.


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