基于决策树的多角度遥感影像分类

Classification of MISR Multi-Angle Imagery Based on Decision Tree Classifier

快速准确地获取土地利用/覆被信息是遥感领域研究的一个热点课题。本文用5种决策树分类器及MISR多角度数据,对塔里木河下游地区进行土地覆被分类研究。通过对不同波段和观测角数据组合形成的6个数据集进行分类比较发现:(1)无论使用哪种分类器,相比于天底角观测方式,多角度观测都能获得更高的分类精度,特别是能显著提高灌木、林地和草地类型的分类精度,说明多角度观测能有效地反映地物的反射异质性信息,更好地区分地物。(2)与MLC分类法相比,决策树算法的分类精度更高,特别是随机森林和C 5.0方法最为突出,说明决策树的分类能力要优于MLC法。使用多角度数据集时,这种差别更明显,说明决策树能更有效地利用多角度信息。(3)4种决策树算法(J48、Random Forest、LMT、C 5.0)使用近红外波段的分类效果好于使用红光波段的分类效果,说明近红外波段能提供更多的地物反射异质性信息。

Accurately obtaining the land use and land cover information has been a hot research focus in the field of remote sensing.It is a feasible way to utilize the new remote sensing data source and the effective classification algorithms. In this study, five decision tree classifiers and the MISR multi-angle data have been implemented to study the land cover classification in the lower Tarim River. Six datasets of different bands and observation angles are classified and compared, and the findings are presented as follows：（1） Compared to the Nadir angle observation, the multi- angle observation can achieve higher classification accuracy and significantly improve the classification accuracy of the shrubs, woodland and grassland in particular. It showed that the multi-angle observation can effectively reveal the anisotropic feature of surface reflectance and get a better classification result.（2） Compared with the MLC method, the classification accuracy of the decision tree algorithms is higher, which is especially evident for the random forest and C5.0 methods, implying that the classification ability of decision tree is better than MLC and is more effective when using multi-angle datasets.（3） The classification effect of near infrared band is better than the red band, which indicates that the near infrared band can provide more information about the anisotropic feature of the surface reflectance.