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地理科学  2012 

遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例

, PP. 434-441

Keywords: 湿地,遥感分类,遗传算法,支持向量机,洪河自然保护区

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Abstract:

湿地遥感分类作为湿地管理、监测与评价的重要手段,受到了广泛的关注。遗传算法(GA)借鉴了生物进化规律进行启发式搜索寻优,支持向量机(SVM)是一种新型的空间数据挖掘方法,二者相结合可以发挥各自的优势,寻找到支持向量机的全局最优参数,从而较准确地对湿地进行遥感分类。以洪河自然保护区为例,采用遗传算法优化的支持向量机方法进行了湿地遥感分类研究。同格网搜索下的支持向量机湿地遥感分类及最大似然监督分类对比,结果表明,遗传算法优化较格网搜索方式总精度提高了7.29%,较最大似然监督分类提高了12.06%,方法改善了沼泽、草地与裸地三种地物间的区分,是湿地遥感分类的有效手段。

References

[1]  Li J, Chen W. A rule-based method for mapping Canada’s wetlandsusing optical, radar and DEM data [J]. International Journalof Remote Sensing, 2005, 26(22): 5 051-5 069.
[2]  ?zesmi S L, Bauer M E. Satellite remote sensing of wetlands[J].Wetlands Ecology and Management, 2002, 10(5): 381-402.
[3]  Stehman S V, Wickham J D, Smith J H, et al. Thematic accuracyof the 1992 National Land-Cover Data for the eastern UnitedStates: Statistical methodology and regional results [J]. RemoteSensing of Environment, 2003, 86: 500-516.
[4]  Zhao H T, Jamie M. Data Mining with SQL Server 2005 [M].New York, USA, JohnWiley & Sons, 2006:6-10.
[5]  Li S J, Wu H, et al. An effective feature selection method for hyperspectralimage classification based on genetic algorithm andsupport vector machine [J]. Knowledge-Based Systems, 2011,24(1): 40-48.
[6]  赵东升,吴正方,商丽娜.洪河保护区湿地生态需水量研究[J].湿地科学,2(2):133~138.
[7]  董树斌, 崔光范, 朱宝光,等. 洪河湿地三江平原生物基因库[M]. 佳木斯: 黑龙江洪河国家级自然保护区, GEF 湿地项目三江平原项目办公室, 2007.
[8]  Vapnik V. The Nature of Statistical Learning Theory [M]. NewYork: Springer-Verlag, 1995.
[9]  Holland J H. Adaptation in Natural and Artificial Systems [M].Ann Arbor: The University of Michigan Press, 1975.
[10]  张树清,陈春,万恩璞. 三江平原湿地遥感分类模式研究[J]. 遥感技术与应用, 1999, 14(1): 54~58.
[11]  Zar J H. Biostatistical Analysis (2nd Edition) [M]. EnglewoodCliffs: Prentice-Hall, 1984.
[12]  骆剑承, 王钦, 马江洪,等. 遥感图像最大似然分类方法的EM改进算法[J]. 测绘学报, 2002, 31(3): 234~239.
[13]  Thomas O, Debasmita M, Navin K C T, et al. An objective analysisof support vector machine based classification for remotesensing [J]. Math Geosci., 2008, 40: 409-424.
[14]  Mitsch W J, Gosselink J G. Wetlands [M]. 2nd ed. New York:Van Nostrand Reinhold, 1993: 507-527.
[15]  Fisher B, Turner R K, Morling P. Defining and classifying ecosystemservices for decision making [J]. Ecological Economics,2009, 68(3): 643-653.
[16]  Zhang S Q, Na X, Kong B,et al. Identifying landscape patterndynamics of Sanjiang plain marsh based on remote sensingtechniques [J].Wetlands, 2009, 29(1): 302-313.
[17]  Wickham J D, Stehman S V, Smith J H, et al. Thematic accuracyof the 1992 National Land-Cover Data for the western UnitedStates [J]. Remote Sensing of Environment, 2004, 91:452-468.
[18]  Wright C, Gallant A. Improved wetland remote sensing in YellowstoneNational Park using classification trees to combineTM imagery and ancillary environmental data [J]. RemoteSensing of Environment, 2007, 107(4): 582-605.
[19]  Su L H. Optimizing support vector machine learning forsemi-arid vegetation mapping by using clustering analysis [J].ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64: 407-413.
[20]  Fogel D B. An introduction to simulated evolutionary optimization[J]. IEEE Transactions on Neural Networks, 1994, 5(1):3-14.
[21]  姚云军, 张泽勋, 秦其明,等. 基于支持向量机的遥感影像湿地信息提取研究[J]. 计算机应用研究, 2008, 25(4): 989~991.
[22]  张策,臧淑英,金竺等. 基于支持向量机的扎龙湿地遥感分类研究[J]. 湿地科学, 2011, 9(3): 263~269.
[23]  Melgani F, Bruzzone L. Classification of hyperspectral remotesensing images with support vector machines [J]. IEEE TransGeosci Remote Sens., 2004, 42(8): 1 778-1 790.
[24]  Foody G M, Mathur A. A relative evaluation of multiclass imageclassification by support vector machines [J]. IEEE TransGeosci Remote Sens, 2004, 6(42): 1335-1343.
[25]  Pal M, Mather P M. Support vector machines for classificationin remote sensing [J]. Int J Remote Sens, 2005, 3(26):1007-1011.
[26]  Chang C C, Lin C J. LIBSVM: A library for support vector machines[EB/OL]. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
[27]  Haralick R M, Shanmugam K, Dinstein I. Texture features forimage classification [J]. IEEE Transactions on Systems, Man,and Cybernetics, 1973, 3: 610-621.

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