Separation Index for Classification Feature Selection in Remote Sensing

• Main Authors: Kuo, Yu-Chuan, 郭育全
• Other Authors: Cheng Ke-Sheng
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/08090145067409791889

碩士 === 國立臺灣大學 === 農業工程學系 === 85 === A divergence-based feature selection scheme was implemented in our study. MSSimagery of SPOT satellite and their textureal features were used for landuseclassification of Tsengwen Reservoir watershed located in southern Taiwan.Our feature selection schemem involves calculation of average divergence forselected classification features. New features were sequentially added to thegroup of already-selected features based on the largest divergence incrementin each calculation iteration. The ratio of divergence(DR) of selected features to that of all features was used to determine the minimum number of features.Then from the ranked feature sequence, we identified those features that shouldbe selected for later landuse calssification. Our results showed that at DR=0.9only 6 out of 12 features were needed in landuse classification. Since featureswere sequentially selected, the class-specific increment of classificationaccuracy contributed by the feature under consideration could be observed. Wefound that for landuse class of betel nut, red band was added after infrared andgreen bands but still largely increased the classification accuracy. Thisindicates that although for most landuse classes the red and green bandreflectance are highly correlated, betel nut spectral features of red and greenbands are not.