基于可见光植被指数的面向对象湿地水生植被提取方法

doi:10.13287/j.1001-9332.201605.002

应用生态学报 ›› 2016, Vol. 27 ›› Issue (5): 1427-1436.doi: 10.13287/j.1001-9332.201605.002

基于可见光植被指数的面向对象湿地水生植被提取方法

井然^1,2, 邓磊^1,2*, 赵文吉^1,2, 宫兆宁^1,2

¹首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048;
²首都师范大学资源环境与地理信息系统北京市重点实验室, 北京 100048

收稿日期:2015-09-29 出版日期:2016-05-18 发布日期:2016-05-18
通讯作者: 19760210@qq.com
作者简介:井然,男,1991年生,硕士研究生. 主要从事遥感地学应用研究. E-mail: 15911157479@163.com
基金资助:
本文由国家国际科技合作专项项目(2014DFA21620)资助

Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

JING Ran^1,2, DENG Lei^1,2*, ZHAO Wen-ji^1,2, GONG Zhao-ning^1,2

¹Ministry of Education Key Laboratory of 3D-Information Acquisition and Application, Capital Normal University, Beijing 100048, China;
²Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

Received:2015-09-29 Online:2016-05-18 Published:2016-05-18

摘要/Abstract

摘要： 利用ESP分割工具确定最佳分割尺度,通过多尺度分割算法创建最优分割影像,基于微型无人机影像数据生成可见光植被指数,从一系列可见光植被指数中选取一组最优植被指数,建立决策树规则,利用隶属度函数对研究区自动分类,生成水生植被分布图.结果表明: 监督分类法的总体精度为53.7%,面向对象分类法总体精度为91.7%,与基于像元的监督分类法相比,面向对象分类法显著改善了影像分类结果,并大大提高了水生植被提取精度,监督分类法的Kappa系数为0.4,而面向对象分类法的Kappa系数为0.9.这表明利用微型无人机数据生成的可见光植被指数结合面向对象分类方法提取水生植被在该研究区是可行的,并能够应用到其他类似区域.

Abstract: Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

井然, 邓磊, 赵文吉, 宫兆宁. 基于可见光植被指数的面向对象湿地水生植被提取方法[J]. 应用生态学报, 2016, 27(5): 1427-1436.

JING Ran, DENG Lei, ZHAO Wen-ji, GONG Zhao-ning. Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.[J]. Chinese Journal of Applied Ecology, 2016, 27(5): 1427-1436.

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基于可见光植被指数的面向对象湿地水生植被提取方法

Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

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