地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (3): 452-463.doi: 10.12082/dqxxkx.2020.190247
曹泽涛1,2,3, 方子东1,2,3, 姚瑾4, 熊礼阳1,2,3,*()
收稿日期:
2019-05-23
修回日期:
2019-12-09
出版日期:
2020-03-25
发布日期:
2020-05-18
通讯作者:
熊礼阳
E-mail:xiongliyang@163.com
作者简介:
曹泽涛(1997— ),男,江苏扬州人,硕士生,主要从事研究DEM数字地形分析研究。E-mail:zetao_cao_1997@163.com
基金资助:
CAO Zetao1,2,3, FANG Zidong1,2,3, YAO Jin4, XIONG Liyang1,2,3,*()
Received:
2019-05-23
Revised:
2019-12-09
Online:
2020-03-25
Published:
2020-05-18
Contact:
XIONG Liyang
E-mail:xiongliyang@163.com
Supported by:
摘要:
地貌分类在指导人类建设活动的规模与布局中有着重要的意义。然而,传统的基于数字高程模型(DEM)的地貌分类方法使用的地形因子和考虑到的地貌特征往往比较单一。本文提出了一种基于流域单元的地貌分类方法,该方法考虑了流域单元的多方面特征,包括基本地形因子统计量、地形特征点线统计量、小流域特征和纹理特征。本研究首先基于DEM进行水文分析将研究区域划分成不同的小流域。然后利用数字地形分析提取29个不同方面的特征来表征流域的形态,并基于随机森林(RF)算法进行了特征选择和参数标定。RF是一种基于决策树算法的集成分类器,能有效地处理高维数据,分类精度高。最后选择训练集小流域对RF分类器进行训练,使用训练完成的分类器对整个研究区域的地貌进行分类,研究地貌分异的规律。该实验在我国陕北黄土高原典型黄土地貌区域的地貌分类中取得了较好的结果,结果表明不同的地貌之间存在明显的区域界线,特定的地貌类型在空间上表现出明显的聚集性。通过人工判读进行验证的分类精度达到了85%,Kappa系数为0.83。
曹泽涛, 方子东, 姚瑾, 熊礼阳. 基于随机森林的黄土地貌分类研究[J]. 地球信息科学学报, 2020, 22(3): 452-463.DOI:10.12082/dqxxkx.2020.190247
CAO Zetao, FANG Zidong, YAO Jin, XIONG Liyang. Loess Landform Classification based on Random Forest[J]. Journal of Geo-information Science, 2020, 22(3): 452-463.DOI:10.12082/dqxxkx.2020.190247
表3
RF分类结果与人工判读结果的混淆矩阵"
RF分类结果 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
T1 | T2 | T2/T1 | T3 | T4 | T4/T3 | T5 | T6 | T7 | T8 | ||
人工判读结果 | T1 | 13 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
T2 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
T2/T1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
T3 | 0 | 1 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | |
T4 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | |
T4/T3 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | |
T5 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 1 | 0 | |
T6 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 13 | 0 | 0 | |
T7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 9 | 0 | |
T8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
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