DOI: 10.1016/j.atmosres.2017.12.006
Scopus记录号: 2-s2.0-85038938622
论文题名: Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data
作者: Lazri M. ; Ameur S.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 203 起始页码: 118
结束页码: 129
语种: 英语
英文关键词: Classification
; MSG-SEVIRI
; Network neural
; Radar image
; Random forest
; Support vector machine
Scopus关键词: Classification (of information)
; Decision trees
; Neural networks
; Pixels
; Radar
; Radar imaging
; Rain
; Support vector machines
; Synthetic aperture radar
; Ground radars
; Meteosat second generations
; MSG-SEVIRI
; Overall accuracies
; Random forests
; Spectral feature
; Spinning Enhanced Visible and Infrared Imager
; Whole process
; Image enhancement
; accuracy assessment
; artificial neural network
; atmospheric convection
; classification
; Meteosat
; pixel
; radar imagery
; satellite data
; SEVIRI
; support vector machine
; synthetic aperture radar
英文摘要: A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108963
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Laboratory LAMPA (Laboratoire d'Analyse et Modélisation des Phénomènes Aléatoires), University Mouloud MAMMERI of Tizi Ouzou, Algeria
Recommended Citation:
Lazri M.,Ameur S.. Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data[J]. Atmospheric Research,2018-01-01,203