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Principal component analysis applied to interpretation of aerial images
Author(s): Gerard Brunet; Philippe Durand; Abdellah Qannari; Dariush Ghorbanzadeh
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Paper Abstract

The aim of this study is to determine in aerial images the most significant parameters to automatic recognition of objects or textures such as house roof, vegetation, Ã Statistical method is Principal Component Analysis, a technique for classification and for examining relationships among quantitative variables. For Image Processing, Java Language is used. For statistics, SAS programs (Statistical Analysis System) with IML package (Interactive Matrix Language). Aerial Images are 400*400 pixels with 10 pixels for 1 meter on ground, with the three r,g,b components (red,green,blue). With image a first simple example and fast operator is performed to find contours, then algorithm has been adapted to dene regions with growing and merging of zones. On those zones a set of parameters is calculated to perform Principal Component Analysis for summarizing data. Results of this method allow classification of zones and find the most significant parameters to be selected, levels of r,g,b components, dispersion of those components, and one calculated value in relation with geometric characteristics of each zone.

Paper Details

Date Published: 6 May 2019
PDF: 5 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691B (6 May 2019); doi: 10.1117/12.2524458
Show Author Affiliations
Gerard Brunet, Univ. de Poitiers (France)
Philippe Durand, Conservatoire National des Arts et Métiers (France)
Abdellah Qannari, Univ. de Poitiers (France)
Dariush Ghorbanzadeh, Conservatoire National des Arts et Métiers (France)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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