摘要
机械性破损面容易引发水土流失、次生地质灾害等生态环境问题,但目前还缺乏其基于遥感影像的有效提取方法。选择机械性破损面分布密集的云南省螳螂川流域为研究对象,基于高分二号(GF-2)遥感影像,探讨其基于纹理特征辅助的面向对象提取方法。根据7类地物特征建立地物分类规则,在最优尺度分割的基础上,基于光谱特征的决策树A和基于“光谱+纹理”特征的决策树B进行面向对象的分类。经过精度评价分析得出,相对于传统的监督分类法和仅基于光谱的面向对象分类法,基于“光谱+纹理”特征的决策树B分类方法使Kappa系数和总精度分别提高至0.82和86.25%,有效地提高了机械性破损面的提取精度。
Mechanical damaged surface tends to cause soil erosion,secondary geological hazards and other ecological environment problems,but there is still a lack of effective extraction methods based on remote sensing images.Based on the GF-2 remote sensing image,the authors studied the object-oriented extraction method based on texture features in Tanglangchuan watershed with densely distributed mechanical damage surface.According to the seven types of features,the classification rules were established.On the basis of the optimal scale segmentation,the decision tree A based on spectral features and the decision tree B based on"spectral+texture"features are classified in object-oriented way.Precision evaluation and analysis show that,compared with the traditional supervised classification method and the spectral-based object-oriented classification method,the classification method improves the Kappa coefficient and the total accuracy to 0.82 and 86.25%,respectively,and also effectively improves the extraction accuracy of mechanical damage surface.
作者
夏既胜
马梦莹
符钟壬
XIA Jisheng;MA Mengying;FU Zhongren(School of Earth Science, Yunnan University, Kunming 650500, China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第2期26-32,共7页
Remote Sensing for Land & Resources
基金
国家自然科学基金项目“云南金沙江流域典型区机械破损面空间格局变化与生态响应”(项目编号:41461103)资助。
