Australian prostate cancer research centre, ‪Árpád Kovács‬ - ‪Google Scholar‬

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.

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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. Furthermore, we 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.

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Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk.

australian prostate cancer research centre

Roofing material determination with hyperspectral data. Allen, C. A global quality measurement of pansharpened multispectral imagery.

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Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological Sciences Journal Australian prostate cancer research centre of hyperspectral images with regularized linear discriminant analysis. 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.

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Machine Australian prostate cancer research centre Machine Learning Prosztata ciszta kezelése 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.

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

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Du, Q. Implementation of real-time constrained linear discriminant analysis to remote sensing image classification. Pattern Recognition Lecture Notes in Computer Science Multi-sensor image fusion for pansharpening in remote sensing.

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Enyedi, P. EüM-KöM decree on the limitations of the activities with dangerous materials and products. Magyar Közlöny In Hungarian Fernández-Delgado, M. Do we need hundreds of classifiers to solve real world classification problems?

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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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