Feature extraction for hyperspectral data based on MNF and singular value decomposition

Feature extraction acts a crucial role in application and research of hyperspectral data and minimum noise fraction (MNF) is one of the most common methods in feature extraction. Estimation of noise covariance matrix is an inevitable step of MNF, but it would bring error which would lead imprecise while using the first some components of MNF transform to represent original data. To solve the problem above, a feature extraction method based on MNF and singular value decomposition was proposed. MNF was first acted on data, then, the transformed data were decomposed by singular value decomposition, and the first some components of reconstruction used singular values and singular vectors were selected as feature components of original data. Experiments with factual hyperspectral data indicated that classified precision after feature extraction by the proposed method was higher than those by traditional MNF in three classified methods and different dimension.