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- W2385196629 abstract "Multi-source feature data can be used to improve the accuracy of land cover classification. However, selecting suitable features is an important step. Gini index was applied to select features from the feature set including multi-scale texture, the components of principal component analysis, and terrain data. The selected features and multi-band data were classified into six classes by support vector machine, neural network classifier and maximum likelihood classifier. The results showed that Gini index could select features successfully, and the classification accuracies were improved while the run time was reduced after feature selection." @default.
- W2385196629 created "2016-06-24" @default.
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- W2385196629 date "2009-01-01" @default.
- W2385196629 modified "2023-09-22" @default.
- W2385196629 title "Multi-source feature data selection for land cover classification using remote sensing image" @default.
- W2385196629 hasPublicationYear "2009" @default.
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