Open AccessA Feature Selection Method based on the Pearson’s Correlation and Transformed Divergence Analysis
Author(s) -
Ying Zhang,
Lin Wang,
Danyang Geng,
Yunfei Ai,
Wei Xia,
Xuejiao Bai,
Shikai Sun
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1284/1/012001
Subject(s) - pearson product moment correlation coefficient , feature selection , pattern recognition (psychology) , vegetation (pathology) , panchromatic film , feature (linguistics) , divergence (linguistics) , selection (genetic algorithm) , correlation coefficient , correlation , mathematics , artificial intelligence , computer science , statistics , image resolution , medicine , linguistics , philosophy , pathology , geometry
An improved feature selection method has been presented, which is based on Transformed Divergence (TD) considering weights of classes and Pearson’s correlation analysis. Using the improved method, this study evaluated several derived vegetation indices and texture measures based on Landsat-8 OLI data to determine their effect on improving the land cover classification separability in Jiangle county, Sanming city of Fujian province, China. The best vegetation indices combination was selected by the improved feature selection method and likewise, best textural combinations at different spatial resolution levels from multi-spectral bands or panchromatic band were obtained. The improved feature selection method found that a single feature could not maximize the separability of vegetation classes. When selecting four vegetation indices, the separability of vegetation classes can be maxmized significantly; and two textural measures were suitable for maxmizing the separability of vegetation classes. Overall, the result verifies that the feature selection method considering weights of classes and Pearson’s correlation coefficient can select optimal features to maximize class separability.