地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (1): 124-132.doi: 10.3724/SP.J.1047.2016.00124

• 遥感科学与应用技术 • 上一篇    下一篇

基于投影寻踪学习网络算法的植物群落高分遥感分类研究

杜欣1(), 黄晓霞1,*(), 李红旮1, 沈利强2   

  1. 1. 中国科学院遥感与数字地球研究所,北京 100101
    2. 深圳规划国土发展研究中心,深圳 518040
  • 收稿日期:2015-01-16 修回日期:2015-03-06 出版日期:2016-01-10 发布日期:2016-01-10
  • 通讯作者: 黄晓霞 E-mail:duxin-1321@163.com;hxx@irsa.ac.cn
  • 作者简介:

    作者简介:杜欣(1989-),女,硕士生,研究方向为生态遥感应用。E-mail: duxin-1321@163.com

  • 基金资助:
    深圳市基本生态控制线专项调查;深圳市2012年测绘地籍工程计划项目([2012]0365)

Research on Classification of Plant Community Using Projection Pursuit Learning Network Algorithm on High Resolution Remote Sensing Images

DU Xin1(), HUANG Xiaoxia1,*(), LI Hongga1, SHEN Liqiang2   

  1. 1. Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China
    2. Planning and Land Development Research Center of Shenzhen Shenzhen 518040, China
  • Received:2015-01-16 Revised:2015-03-06 Online:2016-01-10 Published:2016-01-10
  • Contact: HUANG Xiaoxia E-mail:duxin-1321@163.com;hxx@irsa.ac.cn

摘要:

传统的植物群落调查方法主要是野外样地调查和抽样统计,其对于地形复杂的区域难以做到对数据的全面调查;将遥感技术应用于植物群落调查,可实现数据的全面获取,以及对植物群落的快速分类。在深圳市植物群落野外样地调查的基础上,本文应用高分辨率Pléiades影像,结合光谱、地形及纹理信息,采用投影寻踪学习网络的方法,实现了深圳市东部地区植物分类。在实验中,选取人工林和次生林中典型群落样本,将投影寻踪与学习网络算法结合应用于植被分类,通过分类结果与经典监督分类方法比较表明,该算法应用于植物群落分类是可行的;并且该算法分类精度高,更新速度快,能满足深圳市重点项目基本生态控制线专项调查的要求。

关键词: 投影寻踪, 学习网络, 高分辨率遥感影像, 植物群落分类

Abstract:

Plant community is a significant content in the ecosystem. Traditional investigation method for plant community is mainly based on statistical sampling, which is limited by the data acquisition from complex terrain areas. In contrast, high-resolution remote sensing technique provides a convenient way to quickly access data in a large area. To overcome the shortcomings derived from the high dimensional features, which is caused by related data increasing, we choose the algorithm of projection pursuit learning network (PPLN) along with field samples of typical plant communities to realize a fast classification on the vegetation in the east of Shenzhen. Then,in the experiment, the spectral and texture information extracted from Pléiades images, and the terrain interpolated from topographic map are selected and used to build high dimensional features, which is crucial to the vegetation classification using remote sensing images. The learning network for projection pursuit is applied to discriminating the typical communities in both plantation and natural secondary forest in the study area. Compared with Maximum-likelihood classification (MLC) and Support Vector Machine (SVM), PPLN can achieve more accurate results for plant community classification. As a conclusion, the plant community classification with PPLN meets the requirements of the investigation project, achieves the quick updating of some basic information related to forest resources, and looks forward to involve in some other ecological research as well.

Key words: projection pursuit, learning network, high resolution remote sensing, plant community classification