基于图像欧氏距离的高光谱图像流形降维算法

提出两种基于图像欧氏距离的非线性降维方法.该方法利用高光谱图像物理特性,将图像欧氏距离引入到传统的流形降维算法中.与其它应用于高光谱图像的降维算法相比,该算法具有诸多优点.图像欧氏距离的引入,在考虑高光谱图像本身的空间关系的同时,很好地保持了数据点之间的局部特性,可以实现有效地去除原始数据集光谱维和空间维的冗余信息.实际高光谱数据的实验结果表明,该算法应用于高光谱图像分类时,与其它常见的方法相比具有更高的分类精度....

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Bibliographic Details
Published in红外与毫米波学报 Vol. 32; no. 5; pp. 450 - 455
Main Author 陈宏达 普晗哗 王斌 张立明
Format Journal Article
LanguageChinese
Published 复旦大学电子工程系,上海,200433%复旦大学电子工程系,上海200433 2013
复旦大学波散射与遥感信息重点实验室,上海200433
Subjects
分类
图像欧氏距离
非线性降维
高光谱遥感图像
高光谱遥感图像
image Euclidean distance
hyperspectral imagery
图像欧氏距离
classification
非线性降维
分类
nonlinear dimensional reduction
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ISSN1001-9014
DOI10.3724/SP.J.1010.2013.00450

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Summary:提出两种基于图像欧氏距离的非线性降维方法.该方法利用高光谱图像物理特性,将图像欧氏距离引入到传统的流形降维算法中.与其它应用于高光谱图像的降维算法相比,该算法具有诸多优点.图像欧氏距离的引入,在考虑高光谱图像本身的空间关系的同时,很好地保持了数据点之间的局部特性,可以实现有效地去除原始数据集光谱维和空间维的冗余信息.实际高光谱数据的实验结果表明,该算法应用于高光谱图像分类时,与其它常见的方法相比具有更高的分类精度.
Bibliography:hyperspectral imagery; nonlinear dimensional reduction; image Euclidean distance; classification
CHEN Hong-Da, PU Han-Ye , WANG Bin , ZHANG Li-Ming （1. Dept. of Electronic Engineering, Fudan University, Shanghai 200433, China; 2. The Key Laboratory of Wave Scattering and Remote Sensing Information, Fudan University, Shanghai 200433, China）
31-1577/TN
Two nonlinear dimensionality reduction methods were proposed based on image Euclidean distance. Consider- ing the physical characters of hyperspectral imagery, the methods introduced image Euclidean distance into traditional manifold dimensionality reduction. Compared with other methods, our methods have several advantages. The introduc- tion of image Euclidean distance not only considers hyperspectral image＇ s spatial relationship, but also preserves the lo- cal feature of datasets well. Thus the proposed methods can discard efficiently the redundant information from both the spectral and spatial dimensions. The experiment results demonstrated that the proposed met
ISSN:1001-9014
DOI:10.3724/SP.J.1010.2013.00450