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基于KPCA和FCM的HJ-1A星遥感数据分类

DOI: 10.6046/gtzyyg.2013.01.13, PP. 71-76

Keywords: HJ-1A星,主成分分析(PCA),核主成分分析(KPCA),累积贡献率,模糊C均值分类

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Abstract:

为提高对环境与灾害监测预报小卫星1A(HJ-1A)星遥感数据分类的精度,首先将HJ-1A星HSI高光谱数据和CCD多光谱数据进行GS(Gram-Schmidt)融合,然后利用主成分分析法(principalcomponentanalysis,PCA)和核主成分分析法(kernelPCA,KPCA)分别对融合后的高光谱图像进行降维处理。KPCA降维时采用高斯、线性和多项式3种核函数,根据特征提取效果评价结果,选择累积贡献率较大的多项式核函数。最后,分别对融合后的高光谱图像、PCA主成分图像和基于多项式核函数的KPCA主成分图像进行模糊C均值分类。实验结果表明,KPCA对融合后高光谱图像的特征提取得到了较好的效果,同时提高了分类精度和效率。

References

[1]  Garcia V,Sanchez J S,Mollineda R A.Classification of high dimensional and imbalanced hyperspectral imagery data[C]//Lecture Notes in Computer Science.New York:Springer,2011:644-651.
[2]  刘小芳,何彬彬,李小文.基于半监督核模糊c-均值算法的北京一号小卫星多光谱图像分类[J].测绘学报,2011,40(3):301-306. Liu X F,He B B,Li X W.Classification for Beijing-1 micro-satellite's multispectral image based on semi-supervised kernel FCM algorithm[J].Acta Geodaetica et Cartographica Sinica,2011,40(3):301-306.
[3]  杨国鹏,余旭初,刘伟,等.面向高光谱遥感影像的分类方法研究[J].测绘通报,2007(10):17-20. Yang G P,Yu X C,Liu W,et al.Research on hyperspectral remote sensing image classification methods[J].Bulletin of Surveying and Mapping,2007(10):17-20.
[4]  杜卓明,屠宏,耿国华.KPCA方法过程研究与应用[J].计算机工程与应用,2010,46(7):8-10. Du Z M,Tu H,Geng G H.KPCA method research and application process[J].Computer Engineering and Applications,2010,46(7):8-10.
[5]  韩萍,吴仁彪,王兆华,等.基于KPCA准则的SAR目标特征提取与识别[J].电子与信息学报,2003,25(10):1297-1301. Han P,Wu R B,Wang Z H,et al.SAR automatic target recognition based on KPCA criterion[J].Journal of Electronics and Information Technology,2003,25(10):1297-1301.
[6]  蔡静颖,张永,张凤梅,等.优化KPCA特征提取下的FCM算法研究[J].计算机工程与应用,2009,45(32):38-40. Cai J Y,Zhang Y,Zhang F M,et al.Fuzzy c-mean algorithm based on optimized KPCA feature extraction[J].Computer Engineering and Applications,2009,45(32):38-40.
[7]  高恒振,万建伟,粘永健,等.组合核函数支持向量机高光谱图像融合分类[J].光学精密工程,2011,4(4):878-883. Gao H Z,Wan J W,Nian Y J,et al.Fusion classification of hyperspectral image by composite kernels support vector machine[J].Optics and Precision Engineering,2011,4(4):878-883.
[8]  田慧,周绍光.利用改进的FCM方法分割高分辨率遥感影像[J].测绘通报,2011(12):44-57. Tian H,Zhou S G.Segmentation of high resolution image using improved FCM method[J].Bulletin of Surveying and Mapping,2011(12):44-57.
[9]  钮立明,蒙继华,吴炳方,等.HJ-1A星HSI数据2级产品处理流程研究[J].国土资源遥感,2011,23(1):77-82. Niu L M,Meng J H,Wu B F,et al.Research on standard preprocessing flow for HJ-1A HIS level 2 data product[J].Remote Sensing for Land and Resources,2011,23(1):77-82.
[10]  Ambarish J.Non-linear dimension reduction using kernel PCA[EB/OL].[2010-04-20].
[11]  Congalyon R G.A review of assessing the accuracys of classification of remotely sensed data [J].Remote Sensing of Environment,1991,37(1):35-46.
[12]  王华忠,俞金寿.核函数方法及其模型选择[J].江南大学学报:自然科学版,2006,5(4):500-504. Wang H Z,YU J S.Study on the kernel-based methods and its model selection[J].Natural Science Edition of Southern Yangtze University,2006,5(4):500-504.

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