作者
Akhtar Jamil, Bulent Bayram
发表日期
2017/10/9
期刊
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
卷号
11
期号
1
页码范围
89 - 94
出版商
IEEE
简介
Understanding tree species distribution and land use/cover classes plays a key role for developing environmental monitoring and decision support systems. This study investigates a method based on integration of multiple classifiers to improve the classification accuracy for extraction of tree species and land use/cover classes from large scale data. First, a diverse set of classifiers from different families of statistical learning was selected as base classifiers namely: support vector machine, artificial neural network, and random forest. Both spectral and spatial features were, then, extracted and fed into individual classifiers to classify data into four classes (tea gardens, other trees, impervious surfaces, and bare land). Finally, the results obtained from each classifier were combined to obtain final output by maximum voting. The proposed method was evaluated by using an area-based accuracy assessment on a dataset …
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