因子分析模型的高光谱数据降维方法

为解决高光谱遥感数据量大且波段间相关性高等问题,提出基于因子分析模型的高光谱数据降维方法。该方法通过因子载荷矩阵求解、模型参数求解、旋转矩阵计算以及因子得分估计,得到表征高光谱图像的本征维数。该方法可以找出少数的几个综合因子来代表众多因子,而这少数几个综合因子不仅能主要反映原来的众多因子的信息,而且彼此独立,从而实现高光谱数据的降维。通过利用航空推扫型成像光谱仪(PHI)数据进行本文方法的性能验证,结果表明,Kappa系数从未降维数据的0.744提高到0.821,满足了得到数据本征维数的同时最大程度的保留

Dimensional reduction method based on factor analysis model for hyperspectral data

A dimensional reduction method based on the factor analysis model is proposed for hyperspectral data to resolve the problems of high relativity of bands and large volumes of data.The intrinsic dimensions of hyperspectral data can be obtained by our method through further processing,including solving the factor payload matrix, computation of model parameters and rotated matrix,and the estimation of the factor contribution.Less composite factors can be found to replace data of all bands,which can not only represent almost information of original data,but is also factor independent.Push Hyperspectral Imager (PHI) data is used to evaluate the performance of our proposed method.The result illuminates Kappa parameter is improved from 0.744 to 0.821,and all useful information of data is reserved,relativity among bands is removed,and class separability is increased after dimensional reduction.