Hyperspectral classification based on kernel low-rank multitask learning

In this paper, we propose a kernel low-rank multitask learning (KL-MTL) method to handle multiple features from the variational mode decomposition (VMD) domain for hyperspectral (HSI) classification. Core ideas of the proposed method are twofold: 1) a non-recursive VMD method is applied to extract various features (i.e. intrinsic mode functions (IMFs)) of the original data concurrently; 2) KL-MTL is proposed for classification by taking the extracted IMFs as multiple tasks. In KL-MTL, the low-rank representation formulated by nuclear norm can capture global structure of multiple tasks while the kernel tricks are utilized for nonlinear extension of the low-rank multitask learning (MTL). Experimental results using the real hyperspectral data demonstrate that the proposed methods have satisfactory classification performance.