Search results for "Chin"
showing 10 items of 10015 documents
The first example of cofacial bis(dipyrrins)
2016
International audience; Two series of cofacial bis(dipyrrins) were prepared and their photophysical properties as well as their bimolecular fluorescence quenching with C-60 were investigated. DFT and TDDFT computations were also performed as a modeling tool to address the nature of the fluorescence state and the possible inter-chromophore interactions. Clearly, there is no evidence for such interactions and the bimolecular quenching of fluorescence, in comparison with mono-dipyrrins, indicates that C-60-bis(dipyrrin) contacts occur from the outside of the "mouth" of the cofacial structure.
Herbicidal value of essential oils from oregano-like flavour species
2017
ABSTRACTChemical composition and phytotoxicity of oregano, marjoram and Thymus mastichina essential oils against Portulaca oleracea L., Lolium multiflorum Lam. and Echinochloa crus-galli (L.) Beauv. has been investigated. Seventy-seven compounds reaching 97.3% and 99.4% were identified by gas chromatography–mass spectrometry. Carvacrol (60.42 ± 0.07%), p-cymene (15.52 ± 0.02%) and γ-terpinene (5.19 ± 0.02%) were the main compounds in oregano essential oil, whereas large amounts of 1,8-cineol (59.59 ± 0.85%, 49.49 ± 0.37%), linalool (13.05 ± 0.04%, 5.66 ± 0.01%) and α-terpineol (3.36 ± 0.10%, 5.59 ± 0.01%), followed by β-pinene (4.35 ± 0.39, 5.54 ± 0.01%) and α-pinene (4.11 ± 0.53, 4.28 ± 0.…
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
2016
Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squar…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information
2018
Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately.
Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
2021
Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…
Soil development on sediments and evaporites of the Messinian crisis
2020
Abstract Vast areas in the Mediterranean are characterised by evaporite deposits of the Messinian crises (c. 6–5.3 Ma BP). During this period, large deposits were built up in shallow lagoon-like systems and are now found in southern Italy, Albania, Cyprus and Turkey. So far, soil formation on evaporites has been studied predominantly in subarid to arid environments. Although the formation of soils has received new significance, little is known about the evolutional trajectories on evaporites of the Mediterranean. We therefore studied soil formation in the Caltanissetta basin (Sicily) where evaporites are most widespread. The lithologies included the sequence: marine clay deposits, laminated…
GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment
2019
Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. …