Methods for performing dimensionality reduction in hyperspectral image classification
Methods for performing dimensionality reduction in hyperspectral image classification
复制标题
高光谱图像分类中的降维方法
DOI：
10.1177/0967033518756175
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Da‐Wen Sun
中科院分区：
文献类型：
--
作者：
Jun‐Li Xu;C. Esquerre;Da‐Wen Sun
本文提供了几种有效的高光谱图像数据降维策略，并以Matlab计算语言中的命令行脚本作为辅助数据。由于数据降维方法众多，本文将主要讨论高光谱成像中常用的几种降维方法。基于变换的方法包括主成分分析和线性判别分析，波段选择方法包括偏最小二乘回归结合变量在投影得分中的重要性、选择性比和显著性多元相关性，基于蒙特卡罗抽样的方法包括增强蒙特卡罗变量选择和竞争自适应加权抽样，波段选择方法包括多波段选择和多波段选择。基于模型群体分析的libPLS方法，包括无信息变量消除，随机青蛙和PHADIA; Matlab内置的功能，包括Relieff，逐步回归和顺序特征选择;以及遗传算法指导的选择方法。补充材料中包含的示例数据也可供下载，将用于简化决策树模型，以区分鲑鱼片上的白色条纹和红色肌肉像素，因为分类是高光谱成像的主要应用领域之一。在这项工作中，有许多原始的代码和功能，如快速多次散射校正预处理，离群点检测，最佳截止值的确定，尖峰，死光谱识别和校正的高光谱图像。更重要的是，提出了一种基于方差膨胀因子的选择函数来诊断和缓解共线性问题，因为共线性和多重共线性总是被期望在光谱数据中是严重的。在这项工作中，提供了一步一步的程序，这些策略很容易适应个别情况。
This paper provides several useful strategies for performing the dimensionality reduction in hyperspectral imaging data, with detailed command line scripts in the Matlab computing language as the supplementary data. Due to the vast number of data dimensionality reduction methods available, this paper will mainly focus on some commonly used approaches adopted in hyperspectral imaging. In this work, transformation-based methods include principal component analysis and linear discriminant analysis, while band selection methods are comprised of partial least squares regression combined with the variable importance in the projection scores, selectivity ratio, and significance multivariate correlation; Monte Carlo sampling-based methods including enhanced Monte Carlo variable selection and competitive adaptive reweighted sampling; model population analysis-based methods from libPLS including uninformative variable elimination, random frog, and PHADIA; Matlab built-in functions for feature selection including Relieff, stepwise regression, and sequential feature selection; and the selection method guided by genetic algorithm. The example data included in supplementary material, also available for download, will be used to simplify decision tree models for differentiation of white stripe and red muscle pixels on salmon fillets, since classification is one of the main application domains of hyperspectral imaging. In this work, there are many original codes and functions developed, such as fast multiple scattering correction preprocessing, outlier detection, optimal cutoff value determination, spikes, and dead spectra identification and correction for hyperspectral image. More importantly, a further selection function based on variance inflation factor is proposed to diagnose and alleviate collinearity problem because collinearity and multicollinearity are always expected to be severe in the spectral data. In this work, step-by-step procedure is provided for easy adaptation of these strategies to individual case.