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.