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基于支持向量机的MERIS土地覆盖制图及其空间一致性分析()

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

Issue:
2008年06期
Page:
73-78
Column:
研究论文
publishdate:
2008-11-30

Article Info:/Info

Title:
Use of support vector machines algorithm to map MERIS land cover and its spatial agreement analysis
Article ID:
1000-2006(2008)06-0073-06
Author(s):
LI Ming-shi1 Chandra Girl2 ZHU Zhi-liang3 L■ Heng 4 PAN Jie1 WEN Wei-song5 XU Da5 LIU An-xing5
Keywords:
MERIS Decision trees Support vector machines Spatial agreement analysis
Classification number :
ST57
DOI:
10.3969/j.jssn.1000-2006.2008.06.017
Document Code:
A
Abstract:
This study focused on the development and assessment of the Medium Resolution Imaging Spectrometer(MERIS) land cover product. Four supervised classifiers including the Mahalanobis distance, maximum likelihood, decision trees and support vector machines (SVM) were applied to develop land covet’ information following the National Land Cover Database (NLCD) 2001 classification scheme. Results showed that SVM algorithm performed most optimally. The derived MERIS land cover was spatially close to NLCD 2001, although its capability for identifying ground details was less powerful than NLCD 2001. Furthermore, MERIS data were successful at delineating water, evergreen forest, barren land and cultivated crops, and less successful at characterizing deciduous forest and shrub/ scrub. Misclassification of shrub/scrub to barren land, evergreen forest, and grassland were observed in MERIS land cover. However, production of MERIS land cover is much less labor-intensive and cost-effective than that of NLCD2001, so the moderate resolution MERIS land cover may have value for specific applications. Future production of MERIS land cover should adequately use diverse ancillary information and a regionally tuned classification strategy to achieve more reliable results.

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Last Update: 2013-06-21