Kernel Sparse Multitask Learning for Hyperspectral Image Classification With Empirical Mode Decomposition and Morphological Wavelet-Based Features

Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples.