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Keywords: remote sensing, pan-sharpening, asbestos, machine learning Abstract Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We applied a three- and a six-class approach red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts.

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Furthermore, prostatitis a pásztorban applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent.

The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent.

prostatitis a pásztorban

Asbestos was identified successfully with all classifiers. The results can be used by local parot a prosztatitis for roof mapping to build registers of buildings at risk. Roofing material determination with hyperspectral data. Allen, C. A global quality measurement of pansharpened multispectral imagery. Extracting water-related features using reflectance data and principal component analysis of Landsat images.

Hydrological Sciences Journal Classification of hyperspectral images with regularized linear discriminant analysis.

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New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images. International Journal of Remote Sensing Random Forests.

Machine Learning Machine Learning Mastery with R. First edition, Machine Learning Mastery. Fátlan vegetációtípusok azonosítása légi hiperspektrális távérzékelési módszerrel Vegetation mapping in an alkali landscape — application of airborne hyperspectral data.

Tájökológiai Lapok In Hungarian Burai, P. Airborne hyperspectral remote sensing for identification grassland vegetation.

Ajánlások krónikus prosztatagyulladásra Prosztatagyulladás kezelése házilag |

Chhikara, R. Discriminant analysis using certain normed exponential densities with emphasis on remote sensing application. Pattern Recognition 5. Spatial analysis of remote sensing image classification accuracy. Remote Sensing of Environment A review of assessing the accuracy of classifications of remotely sensed data.

Heterogeneous forest classification by creating mixed vegetation classes using EO-1 Hyperion. Du, Q. Implementation of real-time constrained linear discriminant analysis to remote sensing image classification.

Krónikus prosztatagyulladás ajánlások, Krónikus prosztatagyulladás kezelése — Istenhegyi Klinika A prostatitis kezelő orvosok ajánlásai Mi a teendő akkor, ha a gyógyszeres kezelés akár évek múltán sem hozott eredményt? Mikor beszélünk krónikus prosztatagyulladásról? Mit kell tudni a prosztatagyulladásról? Prosztatagyulladás: így enyhíthetőek gyorsan a tünetek Férfiak, akik gyógyították a prosztatagyulladást. A prosztatagyulladás tünetei A prosztatagyulladás tünetei BENU Gyógyszertárak Ajánlások azok számára, akik gyógyították a prosztatagyulladást Azok számára, akik ebben érintettek, a megoldást az életmódváltás jelentheti.

Pattern Recognition Lecture Notes in Computer Science Multi-sensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion 1. Enyedi, P. EüM-KöM decree on the limitations of the activities with dangerous materials and products.

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Magyar Közlöny In Hungarian Fernández-Delgado, M. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research Gibril, M. Hungarian Geographical Bulletin Discriminant analysis for classification of murine melanomas and human cervical epithelial cells. Analytical and Quantative Cytollogy and Histology Hijmans, R. R package version 2. Random Decision Forests. August Kang, D. Annals of Occupational and EnvironmentalMedicine 25 1 Doi: CreateSpace Independent Publishing Platform.

Krówczyńska, M. Mapping asbestoscement roofing with the use of APEX hyperspectral airborne imagery: Karpacz area, Poland — a case study.

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Miscellanea Geographica Geomatics and Environmental Engineering 8. R package version 6. Understanding variable importances in forests of randomized trees.

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Mándi, A. Role of occupational asbestos exposure in Hungarian lung cancer patients. International Archives of Occupational and Environmental Health Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels.

Journal of Stored Products Research The evaluation and prostatitis a pásztorban of an urban land cover map with image data fusion and laboratory measurements. Monitoring the changes of a suburban settlement by remote sensing. Acta Geographica Debrecina Landscape and Environment 5.

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Padwick, C. WorldView-2 Pan-Sharpening. Pal, M. Random forest classifier for remote sensing classification. Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools. Spatial epidemiology risk assessment for rehabilitated former asbestos mining areas in Limpopo Province, South Africa, using remote sensing and conventional analytical methods.

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South African Journal of Epidemiology and Infection Introduction to the Exploration of Multivariate Biological Data. Leiden, Backhuys Publisher.

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Powers, D. Technical Report SIE Adelaide, South Australia. Pranckevičius, T. Baltic Journal of Modern Computing 5. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images.

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European Journal of Prostatitis a pásztorban Sensing R: A language and environment for statistical computing. Vienna, R Foundation for Statistical Computing. Samsudin, S. Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data.

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Journal of Applied Remote Sensing 10 2 LDA vs. Chemometrics and Intelligent Laboratory Systems A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.

BMC Bioinformatics 9. Applied multivariate statistics for the social sciences. Switzer, Prostatitis a pásztorban.