Hyperspectral image data unsupervised classification using Gauss-Markov random fields and PCA principle

Hyperspectral image data (HSI), with hundreds of high resolution spectral bands, are usually utilized for background characterization. Background characterization can be performed supervised or unsupervised. No matter what classification method is used, the reduction of HSI data dimensionality is first conducted to allow effective feature extraction for classification. Principle component analysis (PCA) is generally used to de-correlate data and maximize the information content in a reduced number of features. This maximization of information is based on the covariance matrix of different spectral bands. The principle components generally contain the background of observed terrain. Some small target or small edge is possibly smoothed or lost. PCA projects the data onto the principal directions. Only the correlation of different spectral bands is used during PCA analysis. However, the correlation of different pixels in the terrain also can be used to compress and reconstruct the HSI data. In this paper, the Gauss-Markov random field (GMRF), which is assumed to be the model of observed terrain, and maximum a posteriori (MAP) estimation, are used to compress and construct the HSI data with PCA. Also the unsupervised classification is used to evaluate the result of this method.