螳螂川流域典型区机械性破损面遥感信息提取方法研究

Study on Remote Sensing Information Extraction Method of Mechanical Damage Surface in Tang Langchuan Basin

机械性破损面是指大型机械工程施工后留下的具有纹理特征,尚未进行植被恢复的裸露面。螳螂川流域受露天采矿、工程建设等影响,机械性破损面分布密集,快速准确地提取出其机械性破损面空间分布信息,对于加强流域内生态环境建设和保护具有重要意义。但目前有效提取机械性破损面的方法较少,针对此问题,选择流域内机械性破损面分布密集的典型区为研究对象,以高分二号卫星遥感影像为数据源,采用面向对象分类方法,通过将研究区进行最优尺度分割后,对研究区的典型地物构建模糊分类规则,按地物类型分层提取信息,最终得到研究区地物类型分布图,实现机械性破损面遥感信息提取。运用基于像素文件(TTA Mask)的混淆矩阵方法进行精度评价,分类总体精度达到90%,Kappa系数为0.78。与其他传统分类方法进行比较,分类精度明显提高,说明采用该文方法提取机械性破损面具有较好的可行性。研究成果为快速提取该流域及其他类似区域内的机械性破损面提供技术支撑,并为类似研究提供参考。

Mechanical damage surface refers to the bare surface,which has texture characteristics and has not been restored by vegetation after large-scale mechanical engineering construction.Due to the influence of open-cut mining and engineering construction,the mechanical damage surfaces in Tang Langchuan basin are densely distributed.It is of great significance to extract the spatial distribution information of the mechanical damage surface quickly and accurately,which strengthen and protect the ecological environment construction in the watershed.However,there are currently few effective methods for extracting mechanical damage surfaces.In response to this problem,a typical area with densely distributed mechanical damage surfaces in the basin is selected as the research object.The remote sensing image of GF-2 is used as the data source and the object-oriented classification method is adopted.After dividing the research area with the optimal scale,fuzzy classification rules are constructed for the typical features in the research area.The information is extracted hierarchically according to the type of the features and got the object types in the study area distribution,achieving mechanical damage surface of remote sensing information extraction.The confusion matrix method based on pixel file(TTA Mask)was used to evaluate the classification accuracy.The overall classification accuracy reached 90%and the Kappa coefficient is 0.78.Compared with other traditional classification methods,the Maximum Likelihood Classification has the best classification method.The overall classification accuracy is 75%and the Kappa coefficient is 0.66,which is far lower than the accuracy of the proposed method in this paper.There are some errors such as dividing the mechanical damage surface into woodland and traffic land,incomplete road classification and so on,the classification effect is general.By comparing the results with the classification accuracy,the classification accuracy is improved obviously and the misclassification of ground objects is reduced,which indicates that the method proposed in this paper has better feasibility and superiority in extracting mechanical damage surface.The research results provide a technical support for the rapid extraction of mechanical damage surfaces in this basin and other similar areas,providing a reference for similar studies.