Classifying hyperspectral remote sensing imagery with independent component analysis

In this paper, we investigate the application of independent component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm, although the proposed method is applicable to other popular ICA algorithms. The major advantage of using ICA is its capability of classifying objects with unknown spectral signatures in an unknown image scene, i.e., unsupervised classification. However, ICA suffers from computational expensiveness, which limits its application to high dimensional data analysis. In order to make it applicable to hyperspectral image classification, a data preprocessing procedure is employed to reduce the data dimensionality. Noise adjusted principal component analysis (NAPCA) is used for this purpose, which can reorganize the original data information in terms of signal-to-noise ratio, a more appropriate criterion than variance when dealing with images. The preliminary results demonstrate that the selected major components from NAPCA can better represent the object information in the original data than those from ordinary principal component analysis (PCA). As a result, better classification using ICA is expected.