摘要
在空间人造目标光谱分析领域,受到观测距离和观测设备空间分辨率的限制,通常在观测空间人造目标光谱信号时,目标某个瞬时视场中的多种纯物质材料的光谱特征信息组合在一个像元中,形成“混合光谱”。因此,将这些混合光谱分解为每个单一材料的光谱并估计出相应的组成比例是空间人造目标光谱分析研究的重点。大多数现有空间目标光谱分解方法都假设空间人造目标混合光谱中包含的纯物质材料种类个数(即“端元数目”)是先验已知的,这对于未知空间人造目标而言是不现实的。因此,纯物质材料数目正确估计对后续光谱数据分析处理的准确性起着至关重要的作用。目前,现有的端元数目确定方法的设计均在高斯白噪声的假设下进行,而对于噪声信号的分布存在频谱相关性的情况下,会提供较差的结果。采用一种基于数据内在维度和似然最大化理论的方法——鲁棒特征值极大似然方法。由于数据内在维数与信号协方差矩阵和信号相关矩阵特征值差异的统计分布特性高度相关,因此通过分析该特征值差异的统计分布特性,构建一个极大似然函数,可以实现空间人造目标混合光谱端元数目的确定。该方法包含两个步骤:首先,采用基于多元回归和改进最小噪声分离方法对原始光谱数据进行预处理完成噪声特性估计和噪声白化过程,从而有效抑制具有频谱相关性的噪声的干扰;接下来,通过求解一个离散对数联合似然函数的极大值问题来实现空间人造目标混合光谱端元数目的确定,该方法完全不需要输入任何参数,并且运行速度比较快。分别利用实验室实测的五种空间人造目标材料的可见/近红外光谱数据和美国地质勘测局光谱数据构建混合光谱仿真数据进行实验。结果表明,该方法能有效抑制相关噪声和白噪声的干扰,空间人造目标纯物质材料数目确�
When observing the spectral signal of an artificial space target,because of the long observation distance and the low spatial resolution of observation equipment,the spectral signatures of multiple pure materials in a certain instantaneous scene is combined in one pixel to form a"mixed spectrum".Therefore,unmixing these mixed spectra into the collection of pure material spectra and estimating the corresponding fractional abundances have been increasingly significant in the field of spectral analysis for artificial space targets.Most existing spectral unmixing methods assume that the number of pure materials(that is,"the number of endmembers")contained in mixed spectra of an artificial space target is known as a priori,which is unrealistic for unknown artificial space targets.Therefore,the exact estimation of the number of endmembers plays a significant role in the accuracy of subsequent spectral analysis and processing.At present,the existing methods of endmember number estimation are mostly proposed under the assumption of Gaussian white noise interference.However,when the distribution of the noise signal is a spectral correlation,poor estimation results will be provided.In this paper,based on the intrinsic dimensions of data and the theory of maximum likelihood,a Robust Eigenvalue Maximum Likelihood(REML)method is proposed.By analyzing the statistical distribution characteristics of differences between the eigenvalues of the signal covariance matrix and those of signal correlation matrix,a maximum likelihood function can be established to estimate the number of endmembers contained in mixed spectra.This method consists of two steps:first,the original spectral data is pre-processed using multiple regression and a modified minimum noise fraction method to complete the noise estimation and whitening process,thereby effectively suppressing the interference of spectrally correlated noise.Then,the number of endmembers is estimated by solving a discrete logarithmic maximum likelihood function.This method does not requ
作者
李庆波
苗兴晋
LI Qing-bo;MIAO Xing-jin(Key Laboratory of Precision Opto-Mechatronics Technology,Ministry of Education,School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2021年第5期1607-1611,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61575015)资助。
关键词
空间人造目标
光谱分析
端元数目确定
内在维度
Artificial space target
Spectral analysis
Number of endmembers
Intrinsic dimension
