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|Table of Contents|

高分辨率遥感图像森林训练样本自动提取及其在变化检测中的应用(PDF/HTML)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2015年03期
Page:
13-17
Column:
专题报道
publishdate:
2015-05-30

Article Info:/Info

Title:
Automatic extraction of forest training sample and their application in change detection using high resolution remote sensing image
Article ID:
1000-2006(2015)03-0013-05
Author(s):
ZHANG Lianhua LI Chungan
Guangxi Forest Inventory and Planning Institute, Nanning 530011,China
Keywords:
high resolution remote sense change detection automatic extraction training data integrated forest index
Classification number :
TP751.2
DOI:
10.3969/j.issn.1000-2006.2015.03.003
Document Code:
A
Abstract:
The algorithm of forest training data automation(TDA)has been successfully applied to Landsat images. Taking Guangping Town, Cangwu County, Guangxi Province as the study area, we selected the ALOS image of 2007 and the RapidEye image of 2011 to explore the algorithm’s application in high resolution remote sensing images. The pure forest training samples were automatically identifed at first, and the change detection result was then obtained by the forest/non-forest classification which extracted by the normalized integrated forest index image involved in the anlaysis.The accurate evaluation results showed that the total area error was -2.6% and the spatial location accuracy was 87.7%. It was shown that this algorithm could be effectively applied to high resolution remote sensing images to extract pure forest training samples for the forest/non-forest classification and change detection as the original data.

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Last Update: 2015-05-30