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Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification TEXT SIZE: A A A

Based on an ASTER VNIR image, we studied the applicability of the MML-EM (Minimum Message Length Criterion-Expectation Maximization) algorithm for land-use classification in southern Austria. Firstly, the RVI (ratio vegetation index) and PC1 (first principal component) bands have been utilized to enhance the targeted information; secondly, the MML-EM algorithm and the terrain analysis-based imagery clipping were jointly used for surface type discrimination. Findings showed that the MML-EM method can provide refined imagery classification results and this is the first time it has been applied in this realm.

Publication name

 JOURNAL OF SPATIAL SCIENCE, 10.1080/14498596.2019.1570478

Author(s)

 Zhao, Bo; Yang, Fan; Zhang, Rongzhen; Shen, Junping; Pilz, Juergen; Zhang, Dehui

Corresponding author(s) 

 YANG Fan 
 yangfan@igge.cn  
 Beijing Inst Geol Mineral Resources, Beijing, Peoples R China
 Chinese Acad Geol Sci, Key Lab Geochem Cycling Carbon & Mercury Earths C, Inst Geophys & Geochem Explorat, Langfang, Peoples R China
 Chinese Acad Sci, State Key Lab Ore Deposit Geochem, Inst Geochem, Guiyang, Guizhou, Peoples R China

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