摘要
由于多重反射和散射,高光谱图像中的混合像元实际上是非线性光谱混合。传统的端元提取算法是以线性光谱混合模型为基础,因此提取精度不高。针对高光谱图像的非线性结构,提出了基于图像欧氏距离非线性降维的高光谱遥感图像端元提取方法。该方法结合高光谱数据的物理特性,将图像欧氏距离引入拉普拉斯特征映射进行非线性降维以更好地去除高光谱数据集中冗余的空间信息和光谱维度信息,然后对降维后的数据利用寻找最大单形体体积的方法提取端元。真实高光谱数据实验表明,提出的方法对高光谱图像端元提取具有良好的效果,性能优于线性降维的主成份分析算法和原始的拉普拉斯特征映射算法。
Mixed pixel in hyperspectral image is actually nonlinear mixing of endmembers,which is caused by multiple reflectances and scattering. The traditional endmember extraction algorithms based on linear spectral mixture model perform poorly in finding the correct endmembers. Considering the physical characters of hyperspectral imagery,a new method is proposed to introduce image Euclidean distance into Laplacian Eigenmaps for nonlinear dimension reduction. The proposed method can discard efficiently the redundant information from both the spectral and spatial dimensions. Endmembers are extracted by looking for the largest simplex volume from low-dimensional space. Experimental results demonstrate that the proposed method outperforms the PCA and Laplacian Eigenmaps algorithm.
出处
《电光与控制》
北大核心
2016年第4期48-52,共5页
Electronics Optics & Control
基金
河南省重点科技攻关计划项目(122102210243)
光电成像技术与系统教育部重点实验室开放基金(2014IOFOE01)
关键词
图像处理
高光谱图像
端元提取
非线性降维
图像欧氏距离
拉普拉斯特征映射
image processing
hyperspectral imagery
endmember extraction
nonlinear dimensional reduction
image Euclidean distance
Laplacian Eigenmaps
