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|Table of Contents|

面向对象森林分类的多分类器结合方法研究

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

Issue:
2010年01期
Page:
73-79
Column:
研究论文
publishdate:
2010-01-30

Article Info:/Info

Title:
Combination multiclassifier for objectoriented classification of forest cover
Author(s):
LI Chungan1 SHAO Guofan2
1.Guangxi Forest Inventory & Planning Institute, Nanning 530011, China; 2.Department of Forestry and Natural Resources,Purdue University, West Lafayette IN47906, USA
Keywords:
objectoriented SPOT5 image forest cover classification multiclassifier
Classification number :
S757
DOI:
10.3969/j.jssn.1000-2006.2010.01.016
Document Code:
A
Abstract:
Aimed toward improve the accuracy of objectoriented classification using SPOT5 imagery, three multiclassifier combination methods, include voting rule, Bayesian mean and fuzzy fusion rule, were discussed and a new fusion approach named votingfuzzy rule was developed, which synthesized conservative voting rule and fuzzy fusion rule. Five classifiers include minimums distance, Mahalanobis distance, Bayes rule, fuzzy logic and support vector machine, were involved in the combination. The result indicated that the votingfuzzy rule had higher total accuracy and Kappa index than three other combination rules, and also the Bayes rule, the best single classifier in all classifiers; furthermore, it reduced the difference of producer accuracy between classes. However, the combination effect wasn’t as obvious as indicated in literatures. The reason might owe to the high correlativity in the outputs of five classifiers, for them shared with a sample set, and the protocol with twenty two classes. Thus classifiers shouldn’t be trained with a same training sample set, or might select difference object features in practical application.

References

[1]Lu D, Weng Q. A survey of image classification methods and techniques of improving classification performance[J]. International Journal of Remote Sensing, 2007, 38(5-6): 823-870.
[2]Tso B, Mather P M. Classification Methods for Remotely Sensed Data[M]. New York: Taylor and Francis Inc, 2001.
[3]Franklin J, Phinn S R, Woodcock C E, et al. Rationale and conceptual framework for classification approaches to assess forest resources and properties[G]// Wulder M, Franklin S. Methods and Applications for Remote Sensing of Forests: Concepts and Case Studies. Boston: Kluwer Academic Publishers, 2003.
[4]Fauvel M, Chanussot J, Benediktsson J A. Decision fusion for the classification of urban remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2828-2838.
[5]Pal M, Mather P M. An assessment of the effectiveness of decision tree methods for land cover classification[J]. Remote Sensing of Environment, 2003, 86(4): 554-565.
[6]Lu D, Weng Q. Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+imagery[J]. Photogrammetric Engineering and Remote Sensing, 2007, 70(9): 1053-1062.
[7]柏延臣,王劲峰. 结合多分类器的遥感专题数据专题分类方法研究[J]. 遥感学报,2005,9(5):555-563.
[8]张秀英,冯学智,刘伟. 基于多分类器结合的IKONOS影像城市植被类型识别[J]. 东南大学学报:自然科学版,2007,37(3):399-403.
[9]Benediktsson J A, Kanellopovlos I. Classification of multisource and hyperspectral data based on decision fusion[J]. IEEE Transactions on Geosciences and Remote Sensing, 1999, 37(31): 1367-1377.
[10]Warrender C E, Augusteijn M F. Fusion of image classification using Bayesian techniques with Markov rand infields[J]. International Journal of Remote Sensing, 1999, 20(10): 1987-2002.
[11]Steele B M. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping[J]. Remote Sensing of Environment, 2000, 74(3): 545-556.
[12]Huang Z, Lees B G. Combining nonparametric models for multisource predictive for forest mapping[J]. Photogrammetric Engineering and Remote Sensing, 2004, 70(4): 415-425.
[13]Liu W, Gopal S, Woodcoock C E. Uncertainty and confidence in land cover classification using a hybrid classifier approach[J]. Photogrammetric Engineering and Remote Sensing, 2004, 70(8): 963-972.
[14]Xu L, Krzyzak A, Suenc C Y. Methods of combining multiple classifiers and their applications to handwriting recognition[J]. IEEE Transactionon Systems, Man, and Cybernetics, 1992, 22(3): 418-435.
[15]Ghosh J, Tumer K, Beck S, et al. Integration of neural classifiers for passive sonar signals[G]//Leondes C T. Control and Dynamic Systems—Advances in Theory and Applications. Londen: Academic Press, 1996.
[16]Cappelli R, Majo D, Maltoni D. Combining fingerprint classifiers[G]//Kittler Roli. Multiple Classifiers System. Berlin: Springer, 2000.
[17]贾永红,李德仁. 多源遥感影像像素级融合分类与决策级分类融合法的研究[J].武汉大学学报:信息科学版,2001,26(5):430-434.
[18]Oussalah M. Study of some algebraical properties of adaptive combination rules[J]. Fuzzy Set and System, 2000, 114(2): 391-409.
[19]李崇贵,李春干. 森林资源调查林区GPS控制网的试验研究[J]. 林业科学,2005,41(1):19-24.
[20]杨丽英,覃征,张选平. 分类器模拟算法及其应用[J]. 西安交通大学学报,2005,39(12):1311-1314.

Last Update: 2010-02-09