Empirical mode decomposition based decision fusion for higher hyperspectral image classification accuracy

This paper proposes a novel Empirical Mode Decomposition (EMD) based decision fusion approach for accurate classification of hyperspectral images. The proposed method consists of three steps. In the first step, EMD, which iteratively decomposes the data into so called Intrinsic Mode Functions (IMFs) in accordance with the intrinsic characteristics of data, is applied to each hyperspectral image band for decomposition. In the second step, the IMFs are assumed as different representations of data, and original hyperspectral data as well as IMF based representations are classified by Support Vector Machine (SVM), independently from each other, to obtain independent decisions. In the final step, these independent decisions are fused by a decision fusion rule to get the final classification result. Provided experimental results demonstrate that the proposed EMD based decision approach results in improved SVM classification.