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Acta Geophysica

Tytuł artykułu

Volcanic ash cloud detection from MODIS image based on CPIWS method

Autorzy Liu, L.  Li, Ch.  Lei, Y.  Yin, J.  Zhao, J. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.
Słowa kluczowe
PL obraz teledetekcyjny   analiza głównych składowych   analiza składowych niezależnych   technika wektorów podtrzymujących   chmura pyłu wulkanicznego  
EN remote sensing image   principal component analysis (PCA)   independent component analysis (ICA)   support vector machine (SVM)   volcanic ash cloud  
Wydawca Instytut Geofizyki PAN
Czasopismo Acta Geophysica
Rocznik 2017
Tom Vol. 65, no. 1
Strony 151--163
Opis fizyczny Bibliogr. 36 poz.
autor Liu, L.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
autor Li, Ch.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China,
autor Lei, Y.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
autor Yin, J.
  • Earthquake Administration of Shanghai Municipality, Shanghai, China
autor Zhao, J.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
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Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-3a18b8ce-01b8-4dbd-a89b-ed2faae6d615
DOI 10.1007/s11600-017-0013-1