Search results for "Spectral"
showing 10 items of 3116 documents
Transient and near‐edge absorption in YVO 4 crystals
2007
The process of near-edge absorption annealing in air was studied up to 1500C in YVO4. In this annealing process, two stages with activation energies of 0.16 eV and 0.38 eV were obtained. (Should combine to make this one paragraph)In addition to the annealing study, the transient absorption induced by pulsed electron beam excitation (270 keV, 8 ns) was completed on a set of YVO4 samples with different near-edge absorption levels in the spectral region 3.2-3.5 eV. The spectral range from ∼1.25 eV up to 3.0 eV is covered by strong transient absorption. Transient absorption spectra show at least three broad overlapping bands (∼1.3 eV, ∼2.0 eV and ∼3.0 eV). The 3 eV peak position is close to the…
Analysis of Vibrational Circular Dichroism Spectra of Peptides
2020
Vibrational circular dichroism (VCD) is one of the major spectroscopic tools to study peptides. Nevertheless, a full understanding of what determines the signs and intensities of VCD bands of these compounds in the amide I and amide II spectral regions is still far from complete. In the present work, we study the origin of these VCD signals using the general coupled oscillator (GCO) analysis, a novel approach that has recently been developed. We apply this approach to the ForValNHMe model peptide in both α-helix and β-sheet configurations. We show that the intense VCD signals observed in the amide I and amide II spectral regions essentially have the same underlying mechanism, namely, the th…
Remote sensing of sun-induced chlorophyll fluorescence at different scales
2014
In this contribution we present activities and selected results obtained in recent studies and campaigns conducted in the context of the FLuorescence EXplorer (FLEX) mission. FLEX is a candidate mission for the ESA 8th Earth Explorer and large efforts are currently dedicated to the development of an implementation scheme for an accurate mapping of fluorescence from the selected spaceborne sensor and mission configuration. Field and airborne data collected in different experimental campaigns, together with simulated data, have been used to demonstrate the feasibility of fluorescence retrievals and the potential of exploiting high spatial resolution fluorescence maps for a better understandin…
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
2020
Abstract With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, w…
Remote Sensing Image Classification with Large Scale Gaussian Processes
2017
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for…
On the interpretability and computational reliability of frequency-domain Granger causality
2017
This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
2020
Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites. Matching the landmark accurately is of paramount relevance, and the process can be strongly impacted by the cloud contamination of a given landmark. This paper introduces a complete pattern recognition methodology able to detect the presence of clouds over landmarks using Meteosat Second Generation (MSG) data. The methodology is based on the ensemble combination of dedicated support vector machines (SVMs) dependent…
Efficient Nonlinear RX Anomaly Detectors
2020
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks t…
Nonlinear Cook distance for Anomalous Change Detection
2020
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most {\em influential points} in the observed data. Typically, the pixels with largest residuals are decided to be anomalous changes. In order to find the anomalous pixels we consider the Cook distance and propose its nonlinear extension using random Fourier features as an efficient nonlinear measure of impact. Good empirical performance is shown over different multispectral images both visually and quantitatively evaluated with ROC curves.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
2020
Abstract Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-base…