用决策树方法识别红树林

2011 Fourth International Conference on Information and Computing Pub Date : 2011-04-25 DOI:10.1109/ICIC.2011.70

Xuehui Zhang

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引用次数: 3

摘要

由于红树林与水-植被混合像元光谱相似，分类精度一直较低。基于Landsat5/TM影像，通过K-T变换提取绿化率和湿度。与其他研究者常用的NDVI、TM3/TM5、TM5/TM4相比，绿度和湿度能显著提高红树林与水-植被混合像元的可分性。采用决策树方法对红树林分类的Kappa系数和委托误差分别为0.90和7.9%。

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Identification of Mangrove Using Decision Tree Method

The classification accuracy of mangrove is always low due to the similarity of spectra between mangrove and water-vegetation mixed pixels. Greenness and wetness were extracted by K-T transformation based on Landsat5/TM imagery. The greenness and wetness can significantly improve the separability between mangrove and water-vegetation mixed pixels by comparison with NDVI, TM3/TM5,TM5/TM4, which always were employed by other researchers. The Kappa coefficient, commission error of mangrove class were 0.90, 7.9%, respectively, by using decision tree method.

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2011 Fourth International Conference on Information and Computing

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