A new principal component analysis (PCA)-based minimum description length (MDL) type criterion (termed PMDL) is proposed in this paper to solve the key problem of class number selection in unsupervised classification.It is based on the fact that data of different dimensions after PCA transform should be encoded with different code length

as they represent different amount of data variance.We perform unsupervised classification to hyperspectral image by Gaussian mixture modeling

estimate the parameters of the mixture model using the expectation maximization (EM) algorithm in merged operations to data after PCA linear projection

and select the number of components according to the proposed criterion.Experiments on a set of synthetic data verify the new criterion.The whole algorithm performs quite effectively and gives proper class number without any prior information applied to real data.