| 基于二维主成分分析的高光谱遥感图像降维 |
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| 引用本文： | 赵春晖,宋晓玥.基于二维主成分分析的高光谱遥感图像降维[J].黑龙江大学自然科学学报,2009,26(5). |
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| 作者姓名： | 赵春晖 宋晓玥 |
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| 作者单位： | 哈尔滨工程大学信息与通信工程学院,哈尔滨,150001 |
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| 基金项目： | 高等学校博士学科点基金资助(20060217021);;黑龙江省自然科学基金资助(ZJG0606-01);;哈尔滨市优秀学科带头人基金资助(2009RFXXG034) |
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| 摘 要： | 提出一种以高光谱图像分析为目标的基于二维主成分分析的高光谱遥感图像的降维方法。通过多变量线性变换对高光谱数据进行特征提取,应用二维主成分分析的方法对高光谱遥感图像进行降维。对AVIRIS图像应用二维主成分分析的方法,可将能量主要集中在少数几个特征值中,这就为降维提供了可能。计算机仿真结果表明,该算法计算量小,方差小,峰值信噪比(PSNR)、分类准确性均显著提高,MSE有所下降。
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| 关 键 词： | 主成分分析 降维 TDPCA 高光谱 |
TDPCA-based dimensionality reduction of hyperspectral remote sensing images |
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| ZHAO Chun-hui,SONG Xiao-yue.TDPCA-based dimensionality reduction of hyperspectral remote sensing images[J].Journal of Natural Science of Heilongjiang University,2009,26(5). |
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| Authors: | ZHAO Chun-hui SONG Xiao-yue |
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| Institution: | College of Information and Communication Engineering;Harbin Engineering University;Harbin 150001;China |
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| Abstract: | Based on Two Dimensional Principal Analysis(TDPCA)for hyperspectral image analysis,a dimensionality reduction method of hyperspectral images is introduced.The hyperspectral features are extracted by multilinear transformation and the dimensionality reduction is carried out by Two Dimensional Principal Analysis.Since the energy of the AVIRIS images mainly centralizes in a few eigenvalues,it is feasible for hyperspectral images dimensionality reduction.The experimental results show that the method achieved li... |
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| Keywords: | principal component analysis dimensionality reduction TDPCA hyperspectral |
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