The Maximum Noise Fraction (MNF) transformation is frequently used to reduce multi/hyper-spectral data dimensionality. It explores the data finding the most informative features, i.e. the ones explaining the maximum signal to noise ratio. However, the MNF requires the knowledge of the noise covariance matrix. In actual applications such information is not available a priori; thus, it must be estimated from the image or from dark reference measurements. Many MNF based techniques are proposed in the literature to overcome this major disadvantage of the MNF transformation. However, such techniques have some limits or require a priori knowledge that is difficult to obtain. In this paper, a new MNF based feature extraction algorithm is presented: the technique exploits a linear multi regression method and a noise variance homogeneity test to estimate the noise covariance matrix. The procedure can be applied directly to the image in an unsupervised fashion. To the best of our knowledge, the MNF is usually performed to remove the noise content from multi/hyperspectral images, while its impact on image classification is not well explored in the literature. Thus, the proposed algorithm is applied to an AVIRIS data set and its impact on classification performance is evaluated. Results are compared to the ones obtained by the widely used PCA and the Min/Max Autocorrelation Fraction (MAF), which is an MNF based technique.