Resolution Of Bands Research Articles

Wavelet-based super resolution using pansharpened multispectral images

Several remote sensing applications require high-spatial-high-spectral resolution multispectral (MS) images. However, most MS sensors provide low-spatial-high-spectral resolution MS images together with high-spatial-low-spectral resolution panchromatic (PAN) bands. In order to increase the spatial resolution of MS bands to the resolution of PAN images and to obtain high-spatial/spectral resolution MS bands, either MS and PAN images are fused (i.e., pansharpening) or super resolution (SR) is performed using MS bands only. Nevertheless, existing methods do not utilize the available temporal and spatial information together. In this paper, we propose a multiframe SR algorithm using high-spatial/spectral resolution MS images (i.e., pansharpened), taking advantage of both spatial and temporal data, in order to exceed the spatial resolution of the available PAN bands. We first employ a wavelet-based pansharpening method on a set of MS and PAN images captured at different times. Then, we utilize these pansharpened MS bands in a wavelet-based multiframe SR scheme. The proposed method reveals the inter-wavelet-subband relationship of multitemporal images for SR. We demonstrate our results with comparisons on a Landsat 7 ETM+ dataset.

The effect of paraprotein polymerisation on quantitation by capillary zone electrophoresis and Hevylite®.

Up to 3% of patients with monoclonal gammopathies have multiple serum paraproteins. This article investigates whether multiple isotype-matched paraproteins, as seen on capillary zone electrophoresis, are truly biclonal. Serum samples containing multiple isotype-matched paraproteins were treated with the reducing agent dithiothreitol, and capillary zone electrophoresis was performed pre- and post-treatment. Band resolution and effect of resolution on quantitation of paraprotein burden were assessed. The Hevylite® turbidimetric assay was also evaluated for ability to quantify such paraproteins. Among patients with biclonal isotype-matched paraproteins, 23/24 (96%) IgA paraproteins resolved into a single band following treatment with dithiothreitol compared with only 1/12 (8%) IgG paraproteins. Daratumumab therapy accounted for the second band in 5/9 non-resolving IgGκ paraproteins. Where initially quantified as a single IgA 'complex' (multiple bands in close proximity), the single postdithiothreitol band averaged 2.8 g/L less (P<0.001), likely due to inclusion of lower amounts of underlying serum proteins (y = 0.97x-2.03, R2=0.993). Quantitating IgA biclonal isotype matched (n = 58) using the Hevylite® assay gave higher results (P = 0.002) than capillary zone electrophoresis (y = 1.48x-7.13, R2=0.959). In contrast, single IgA paraprotein results (n = 48) did not differ between the two methods (P = 0.466; y = 1.24x-2.74, R2=0.898), suggesting that polymerisation enhances Hevylite® quantitation. These results suggest that disulphide-mediated polymerisation of IgA paraproteins is more common than true biclonal gammopathy and support dithiothreitol treatment of samples with isotype-matched IgA bands before quantifying by capillary zone electrophoresis. The Hevylite® assay should be utilized with caution where polymerisation is likely. Where IgGκ biclonal isotype-matched paraproteins appear on capillary zone electrophoresis, daratumumab therapy should be considered.

Electronic structure examination of the topological properties of CaMnSb2 by angle-resolved photoemission spectroscopy

We have carried out detailed high resolution ARPES measurements and band structure calculations to study the electronic structure of CaMnSb$_{2}$. The observed Fermi surface mainly consists of one hole pocket around ${\Gamma}$ point and one tiny hole pocket at Y point. Strong spectral weight accumulation along the ${\Gamma}$-X direction is observed on the hole-like Fermi surface around ${\Gamma}$ point, suggesting strong anisotropy of the density of states along the Fermi surface. The tiny hole pocket at Y point originates from an anisotropic Dirac-like band with the crossing point of the linear bands lying $\sim$ 10 meV above the Fermi level. These observations are in a good agreement with the band structure calculations. In addition, we observe additional features along the ${\Gamma}$-Y line that cannot be accounted for by the band structure calculations. Our results provide important information in understanding and exploration of novel properties in CaMnSb$_{2}$ and related materials.

Assessment of the relationship between building density and urban heat island using Landsat images in Makassar City

Increasing temperatures in urban areas provide many adverse effects, and such is the potential for fires, air pollution up to the comfort of life in the state capital, and the other problems, with the global warming which occurred in the world, such as the melting of ice on most of the north pole, unpredictable Global warming and the number of flora and fauna habitat damaged. The need for facilities and infrastructure is the main factor that triggers settlements’ development; in general, urban areas often experience densely populated settlements. Therefore, researchers feel the need to monitor the relationship between Urban Heat Island (UHI) in Makassar City. Monitoring of Urban Heat Island needed surface temperature data from Landsat 8 OLI/TIRS with a thermal band resolution of 100m, building density needed NDBI models for data building density. This study analyses the relationship between building density and urban Heat Island using Landsat Imagery Data in Makassar city. The Landsat 8 OLI/TIRS imagery provides to be used to show the Urban Heat Island (UHI) phenomenon in Makassar City. In addition to UHI analysis, Landsat 8, Oil/TIRS imagery provides building density information using the NDBI spectral transformation. The results of the two result models’ determination tests show that Urban Heat Island and Building Density have a very high relationship.

Prediction performance optimization of different resolution and spectral band ranges for characterizing coco-peat substrate available nitrogen

To realize the high-precision measurement of available nitrogen in coco-peat substrate, the influences of near-infrared (NIR) spectral data of different spectral resolution and band ranges on the model detection performances were studied. A flame NIR spectrometer and an ANTARIS FT-NIR spectrometer were used to acquire spectral data of short-wave near-infrared (SW-NIR) band (1000–1660 nm), long-wave near-infrared (LW-NIR) band (1660–2500 nm), and NIR band (1000–2500 nm) of coco-peat substrate samples respectively. Then, the models were established based on the original data and various pre-processing data. Furthermore, the effects of spectral resolution and detection band ranges on available nitrogen NIR spectroscopy detection of coco-peat substrate were studied by comparing the modeling results. The spectral resolution and detection band ranges had significant influences on the NIR spectroscopy detection of coco-peat substrate available nitrogen. In comparison, the model with high-resolution LW-NIR band spectral data yielded the best prediction performance. Specifically, the correlation coefficients and root mean squared errors in calibration set and prediction set were 0.999, 0.996, 3.197 mg/100g, and 5.373 mg/100g, respectively. The ratio of prediction to deviation was 10.895. High-resolution spectral data was useful to improve the model performance of the sample available nitrogen NIR spectroscopy prediction, and the LW-NIR band range was the optimal detection band for coco-peat substrate available nitrogen NIR spectroscopy detection. Therefore, the high-resolution LW-NIR band spectrometer was useful to the development of high-precision NIR spectroscopy detection equipment for available nitrogen in coco-peat substrate.

Evaluating the VNIR-SWIR datasets of WorldView-3 for lithological mapping of a metamorphic-igneous terrain using support vector machine algorithm; a case study of Central Iran

• Evaluating the WV-3 data in lithological mapping of an area through confusion matrix. • Successfully discrimination of lithological units and boundaries with high accuracy. • Verifying the produced map by field observations, petrography, and spectroscopy. • Defining effect of each band of WV-3 in classification accuracy of individual class. • Finding SWIR-7 as the most significant band in raising the classification accuracy. WorldView-3 (WV-3) is the first high spatial resolution and super-spectral commercial satellite which has the capability in improvement of geological mapping. The VNIR-SWIR data of this satellite are evaluated in this study for mapping the lithological units of a metamorphic-igneous terrain in Chadormalu area, Central Iran, by the use of support vector machine (SVM) classification method. Applying principal component analysis (PCA) as an image transformation technique on the WV-3 data, and interpretation of its results, supported by field observations, petrography, and spectroscopy, lead to identifying training areas which are input to the SVM algorithm. This method not only produces a detailed lithological map of all exposed rock units, but also well discriminates diorite-gabbro diorite from granite, and gneissic granite from green schist and garnet mica schist, which are not revealed in 1:100,000 geological map of the area, published by Geological Survey of Iran (GSI), with overall accuracy of 88.36% and the Kappa coefficient of 0.86. Furthermore, the efficacy of each applied band of WV-3 is assessed in promoting classification accuracy using SVM method. Results show that band 7 of SWIR region has increased the overall accuracy 4.81% relative to SWIR dataset, and improves the classification accuracy of green schist and diorite. Moreover, bands 5, 6, and 8 of SWIR dataset are more efficient in improvement of the overall accuracies than other bands. This study concludes that WV-3 data provides a good facility to generate large scale geological maps owing to its high spatial and radiometric resolution and appropriate SWIR bands.

Interlayer excitonic states in MoSe2/MoS2 van der Waals heterostructures

In the present work, we report different interlayer excitonic states of an aligned $\mathrm{Mo}{\mathrm{Se}}_{2}/\mathrm{Mo}{\mathrm{S}}_{2}$ incommensurate van der Waals (vdW) heterostructure (HS). The HS was fabricated by stacking chemical vapor deposited monolayers, and it was studied using photoluminescence (PL) measurements. We observed the emergence of two low-energy peaks in the PL spectrum of the HS measured at 100 K, which were absent in the constituent monolayers. The orbital resolved electronic band structure and the optical absorption considering the electron-hole interaction for these HSs were calculated using first-principles density functional theory simulations, which showed energy band hybridization and the presence of interlayer excitons (ILEs). Based on these observations, the peak at $\ensuremath{\sim}1.57$ eV was assigned to a momentum direct ILE, while the other peak at $\ensuremath{\sim}1.35$ eV showed feeble emission intensity and was assigned to a momentum indirect ILE. The emergence of both of these excitonic peaks in the HS PL spectrum can be attributed to the formation of a spatially periodic moir\'e potential at a nanometer length scale resulting in hybridization. Our results can help to understand the physics of ILEs and to engineer vdW HSs for efficient optoelectronic devices.

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: Challenges and potentials

Despite the importance of remote detection of marine debris, nearly all published studies are focused on either controlled experiments, or Sentinel-2 data with mixed band resolutions that are subject to large uncertainties. To date, key questions such as the following have not been addressed adequately: To what extent can the various forms of marine debris be remotely detected and differentiated through satellite observations in the visible and near infrared (NIR) spectral range, and how? Here, using published reflectance spectra of various types of floating matters, I address these questions through sensitivity analyses, simulations, and spectral analyses of satellite images. While the study is by no means comprehensive, several observations can still be made. First, it appears impossible to remotely detect marine microplastics from all existing and planned optical sensors. This is simply because the contribution of these particles to the sensor signal, even when they are aggregated on the water surface at the reported maximum particle density, is at least 60 times lower than the required signal (~0.2% subpixel coverage) and 20 times lower than the sensor noise for a sensor with a signal-to-noise ratio (SNR) of 200. In contrast, detecting macroplastics and other debris is possible when they form large patches along ocean fronts or windrows. Second, assuming a SNR of 200, discriminating large patches of marine debris from floating algae is only possible with a subpixel coverage of >0.3%. These threshold values are based on the sensor SNRs only, and they represent the lower bounds of detection and discrimination, respectively. The real threshold values above which a detection or discrimination is possible also depend on the observing conditions, and therefore higher. Third, currently, Sentinel-2 MSI (Multi Spectral Instrument) sensors provide an optimal trade between resolution and coverage, yet MSI sensors have SNRs <200, and interpretation of the MSI spectra requires extra caution due to variable spatial resolutions in different bands, among other factors. From the perspective of pure spectroscopy, it is possible to discriminate floating algae from non-algae floating matters but difficult to differentiate the type of the latter (either plastic or non-plastic debris, foam, etc) because different non-algae floating matters all show relatively flat reflectance spectral shapes in the vis-NIR spectral range. Finally, based on these results, recommendations are made on algorithm designs and sensor designs, for example spectral analysis should be performed over the difference spectra to minimize the impact of variable subpixel coverage, and certain spectral bands are more important than others for the remote detection of marine debris.

Estimation of Modulation Transfer Function (MTF) of VNREDSat-1 satellite for image quality assessment by using the permanent test site

MTF is a factor in evaluating image quality with respect to the contrast and sharpness of the payload, thus directly related to spatial resolution. Therefore, ensuring image quality is an important task, especially for small satellites with high spatial resolution. This study uses the slanted-edge method for the artificial permanent test site and VNREDSat-1 image data with spatial resolution of panchromatic band as 2.5 m to make an MTF value estimate. The MTF value during 5 years of operation averages about 0.2 compared to the threshold as 0.08 (for the test site at Salon de Provence, France, the MTF value ranged from 0.16÷0.27 in across-track direction and 0.16÷0.25 in along-track direction; and at the Buon Ma Thuot test site, these values were 0.16÷0.23 and 0.20÷0.24, the reflectivity of these two test sites are similar), proving that image quality is guaranteed throughout the design life of the satellite; moreover, they are also the basis for Vietnam to finalize regulations on validation and calibration of optical satellite systems in the future when we have own test site.

Wave energy resource characterization employing joint distributions in frequency-direction-time domain

Joint and marginal distributions in the frequency, direction, and time domain are employed to demonstrate their value for wave energy resource characterization when full spectra are available. Insights gained through analysis of these distributions support wave energy converter concept design, operation and maintenance. Spatial trends in the wave energy resource and contributing wave energy systems along the continental shelf of the West Coast of the United States are investigated using the most recent two-dimensional wave spectra measurements at four buoys over an eleven year period (2008 to 2018). Resource hot spots and dominant resolved energy resource bands in the frequency-direction-time domain are delineated. Resource attributes, including frequency and directional spreading, and seasonal variability, are characterized using joint distributions and marginal distributions of wave power spectra. North Pacific westerly swells in the winter season, augmented by Aleutian low-pressure southwesterly swells, are the principal suppliers of the dominant resource and main drivers influencing resource attributes. The modification of these systems southward, especially the North Pacific westerly swells, explains the observed spatial resource trends. The dominant resource wave period shifts two seconds to higher wave periods, thirty degrees in the dominant direction band to a more northward orientation, and forward by one month.

DRUG POLYMORPHISM IDENTIFICATION USING FOURIER TRANSFORM-RAMAN SPECTROSCOPY: A COMPARATIVE STUDY OF LAMIVUDINE AND FINASTERIDE DRUGS

Objectives: Maintaining the quality of the pharmaceutical drug product during its shelf life is highly desirable. The crystalline form of the drug having the great thermodynamic stability is essential for the manufacturers in pharmaceutical industry in view of their profit and also for the safety of the customer. Many pharmaceutical drugs have the tendency to exhibit polymorphism which is unwanted for pharmaceutical companies, where they have experienced market shortages due to these unpredicted polymorphic and/or pseudomorphic changes. The property of a drug exhibiting more than one crystal form is considerably regarded as polymorphism and each of the crystalline form has its own physicochemical properties, namely, solubility, heat capacity, melting point, and sublimation point. To relieve this ultimate effect on the drug quality and stability, a prior detection of polymorphism in the final dosage form is highly recommended. Hence, many analytical techniques have been proposed for the detection of polymorphism in pharmaceutical drug products.&#x0D; Methods: Fourier transform (FT)-Raman spectrometer is used for the investigation of drug polymorphism and the instrument is advanced with charge coupled device detectors, ease of sample preparation and handling, mitigation of sub-sampling problems using different geometric laser irradiance patterns and having different optical components of Raman spectrometers.&#x0D; Results: In this work, we carefully studied the Raman spectral patterns for Lamivudine as well as Finasteride drug substances for the detection of polymorphism. Further, we have highlighted the advantages of FT-Raman spectroscopy over other polymorphism detection techniques. For example, Raman spectra showed invariably sharp, well resolved bands compare to IR spectra due to the minor contribution of overtone vibrations in Raman spectra, resulting in much less broadening and a better resolution of bands. Besides, Raman spectroscopy does not suffer from the sampling problems that are common in X-ray powder diffraction, where preferred orientation and specimen displacements are serious restrictions for the application of quantitative method.&#x0D; Conclusion: Here, in this paper, we are presented and compared the experimental results regarding the detection of polymorphism in Lamivudine and Finasteride drugs using FT-Raman spectroscopy, to illustrate the advantages of the technique in the detection of polymorphism over other techniques.

Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model

The Sentinel-2 (S2) constellation provides multispectral images at 10 m, 20 m, and 60 m resolution bands. Obtaining all bands at 10 m resolution would benefit many applications. Recently, many model-based and deep learning (DL)-based sharpening methods have been proposed. However, the downside of those methods is that the DL-based methods need to be trained separately for the 20 m and the 60 m bands in a supervised manner at reduced resolution, while the model-based methods heavily depend on the hand-crafted image priors. To break the gap, this article proposes a novel unsupervised DL-based S2 sharpening method using a single convolutional neural network (CNN) to sharpen the 20 and 60 m bands at the same time at full resolution. The proposed method replaces the hand-crafted image prior by the deep image prior (DIP) provided by a CNN structure whose parameters are easily optimized using a DL optimizer. We also incorporate the modulation transfer function-based degradation model as a network layer, and add all bands to both network input and output. This setting improves the DIP and exploits the advantage of multitask learning since all S2 bands are highly correlated. Extensive experiments with real S2 data show that our proposed method outperforms competitive methods for reduced-resolution evaluation and yields very high quality sharpened image for full-resolution evaluation.

Gravity field mapping using laser-coupled quantum accelerometers in space

The emergence of quantum technologies, including cold atom based accelerometers, offers an opportunity to improve the performances of space geodesy missions. In this context, CNES initiated an assessment study called GRICE (GRadiom\'etrie \`a Interf\'erom\`etres quantiques Corr\'el\'es pour l'Espace) in order to evaluate the contribution of cold atom technologies to space geodesy and to the end users of geodetic data. In this paper, we present mission scenario for gravity field mapping based on a long baseline gradiometer. The mission is based on a constellation of two satellites, flying at an altitude of 373 km, each equipped with a cold atom accelerometer with a sensitivity of $6 \times 10^{-10}$~m.s$^{-2}$.$\mathrm{\tau}^{-1/2}$. A laser link measures the distance between the two satellites and couples these two instruments in order to produce a correlated differential acceleration measurement. The main parameters, determining the performances of the payload, have been investigated. We carried out a general study of satellite architecture and simulations of the mission performances in terms of restitution of the gravity field. The simulations show that this concept would give its best performance in terms of monthly gravity fields recovery under 1000~km resolution. In the resolution band between 1000 and 222~km, the improvement of the GRICE gradient approach over the traditional range-rate approach is globally in the order of 10 to 25\%.

Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution.

Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

Time Line Correlative Spectral Processing for Stratification of Blood Pressure using Adaptive Signal Conditioning

Stratification of Blood Pressure is essential input in most of the cardiovascular diseases detection and prediction and is also a great aid to medical practitioners in dealing with Hypertension. Denoising based on spectral coding is developed based on frequency spectral decomposition and a spectral correlative approach based on wavelet transform. The existing approaches perform a standard deviation and mean of peak correlation in signal conditioning. The artifact filtrations were developed based on thresholding. Filtration of coefficients has an impact on accuracy of estimation and hence proper signal conditioning is a primal need. Wherein threshold is measured with discrete monitoring, time line observation could improve the accuracy of filtration efficiency under varying interference condition. Dynamic interference due to capturing or processing source results in jitter type noises which are short period deviations with varying frequency component. Hence a time-frequency analysis for filtration is adapted for filtration. This paper presents an approach of spectral correlation approach for signal condition in stratification of blood pressure under cuff less monitoring. This presented approach operates on the spectral distribution of finer resolution bands for monitoring signal in denoising and decision making. Existing approaches lacks the capability of loss-less denoising which is efficiently worked out in this paper.

Leaf area index retrieval of urban vegetation based on sentinel-2 data in Zhongshan, South China

Leaf area index (LAI) is an important indicator of vegetation growth and health monitoring. The free access of Sentinel-2 optical satellite data from European Space Agency (ESA) since June 2015 made it possible to determine dynamic monitoring of LAI in a large area and a short re-entry period, owing to its high spatial resolution (up to 10 m) and unique band combination. These features of Sentinel-2 may bring great application potential in urban forestry management and ecological environment monitoring. In case of Zhongshan city in Guangdong Province, based on Sentinels Application Platform (SNAP) software and two scenes of Sentinel-2 data, LAI of the whole city, as well as specific areas, were retrieved and analyzed. The retrieval result was consistent with the ecological spatial pattern and the vegetation protection status in Zhongshan city, also highly consistent with the field LAI measurement in the forest in a reservoir. The result indicates that this method has good applicability and accuracy in urban LAI inversion.

Isolation, quantification and characterisaion of alkaloids from Eulophia ochreata Lindl by HPTLC analysis

In healthcare system, the development and modernization of phytomedicine in phytochemistry play a central and important role in drug development. Isolation and purification of bioactive compounds from naturally occurring substance is of prime importance. A sensitive and reliable high performance thin layer chromatography method has been developed to estimate gallic acid in ethanolic extract of Eulophia ochreata Lindl. Ethanolic extract prepared as applied on silica gel G 60 F254plate. The plate was developed using Toluene: Ethyl acetate: Methanol: Formic acid(6:6:0.4:1.6) as a mobile phase and detection and quantification were performed by densitometric scanning at 275nm. The system was found to give well resolved bands for alcoholic extract having Rf values as 0.8 was matched with the standard Rf values like 0.82. The densitometric chromatogram of HPTLC fingerprint of the alcoholic extract, isolate and standard gallic acid was obtained. The calibration curve of gallic acid was linear over a concentration range (0.2- 2 microg/ml) with a good correlation coefficient (R2= 0.9986) and coefficient of variation as CV- 2.4663%. The method was validated for linearity, precision, specificity and it was found to be precise, reliable, and suitable. The proposed method is simple, rapid, precise and accurate. The method was suitable for qualitative and quantitative analysis of gallic acid in the alcoholic extract of Eulophia ochreata Lindl.

Application of Micro-Raman Spectroscopy for the Quantitative Analysis of Vitrinite Reflectance in Medium and High Rank Colombian Coals

The structural changes and the degree of maturity of three Colombian coals from the Santander and Antioquia provinces were studied using a simple and fast curve fitting method of the RBS parameters (the distance between band G and band D), FWHM - G (total width at half the maximum of the G band) and AR (area of the Raman spectrum), obtained by deconvolution and band resolution of the Micro-Raman spectra. The results were compared by morphological and chemical analysis using a SEM scanning electron microscope coupled with a solid state X - ray EDS detector. A percentage of vitrinite reflectance (% Ro) of 4.3 %, 3.8 % and 0.07 % was obtained, a percentage of carbon by EDS analysis of 91.47 %, 87.78 %, 73.4 %, a morphological description by SEM analysis presenting smooth, rough surfaces and rough-lumpy and pore size between 0.7 µm - 0.8 µm, 1.3 µm - 1.5 µm and 0.2 µm - 0.7 µm for the anthracite, semi-anthracite and bituminous samples, respectively. The results showed that based on this method, it was possible to obtain a model that allows to rapidly analyze and characterize the coal. The results compare very well to those of Colombian coals normalized by other methods to improve the precision of the measurement. The modified method, however, did not offer an appropriate fit for the lower range coal and produced extremely low reflectance values. This effect is possibly related with the molecular structure of organic matter in coal or the presence of fluorescence interference.

A Water Body Extraction Methods Comparison Based on FengYun Satellite Data: A Case Study of Poyang Lake Region, China

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.