Adaptive subspace decomposition for hyperspectral data dimensionality reduction

Abstract

This paper proposed a novel adaptive subspace decomposition (ASD) method for hyperspectral data dimensionality reduction. The new method is mainly based on the criterions of the correlation matrix and the variability ratio of eigenvalues and it can overcome the disadvantages of the conventional Principal Component Analysis (PCA) method. To evaluate the effectiveness of the new method, experiments are conducted on AVIRIS data. The data dimensionality is reduced from 100 to 5 bands. When applied to classification, the results show that the new method keeps more detail information than the conventional PCA method and can get higher classification accuracy.