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FOREST RESOURCES WANAGEMENT ›› 2009, Vol. 0 ›› Issue (1): 107-113.doi: 10.13466/j.cnki.lyzygl.2009.01.020

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Remote Sensing Image Classification Based on SVM Method for Ecological Service Forests

REN Qiong, JIANG Hong, CHEN Jian, LI Tusheng, PENG Shikui, YU Shuquan   

  1. 1. Forest Resources and Environment Colleg, Nanjing Forestry University, Nanjing 210037, Jiangsu Province;
    2. International Ecological Research Cnter, Zhej iang Forestry College, Hangzhou 311300, Zhejiang Province;
    3. Institute of in ternational Earth System Science, Nanjing University, Nanjing 210093;
    4. Ecological Center, Zhijiang Provincial Forestry Department, Hangzhou 310020, Zhejiang Province, China
  • Received:2008-09-27 Revised:2008-12-03 Online:2009-02-28 Published:2021-01-27

Abstract: This paper deals with the RS image classification based on the SVM method, using space characteristic information for classification of IKONOS high spatial resolution images and monitoring of public w elfare forests.Analy sis was conducted on comparison of this method with tradition method. The resultshow s that the RS image classification based on the SVM method can solve the image classification fragmentation, low accuracy etc, and has advantage in study speed, orientation abili ty and expression, etc.The aim of this paper is to discuss a method to inquire into the classification method of public welfare forests with high spatial resolution RS image and providing theoretical basis and data support for the development of forestry information netw ork and “digi tal forestry”.

Key words: forest ecosystem, ecological service forests, SVM, space characteristic, superior super flat surface

CLC Number: