Empirical Mode Decomposition Based Morphological Profile For Hyperspectral Image Classification

The empirical mode decomposition (EMD) based morphological profile (MP), called as EMDMP, is proposed for hyperspectral image classification in this work. The EMD algorithm can well decompose the nonlinear spectral feature vector to intrinsic components and the residual term. To extract the main spatial characteristics and shape structures, the closing operators are applied to the intrinsic components. In contrast, to extract details and more abstract contextual features, the opening operators are applied to the residual component. Finally, a multi-resolution morphological profile is provided with concatenation of the intrinsic components-based closing profile and residual component based opening profile. EMDMP achieves 96.54% overall accuracy compared to 95.15% obtained by convolutional neural network (CNN) on Indian dataset with 10% training samples. In University of Pavia with 1% training samples, EMDMP results in 97.66% overall accuracy compared to 95.90% obtained by CNN.