Hyperspectral Image Supervised Classification Via Multi-View Nuclear Norm based 2D PCA Feature Extraction and Kernel ELM

In this paper, we propose a novel flexible framework for hyperspectral image (HSI) classification using multi-view spectral-spatial feature extracted by nuclear norm based 2D PCA. We first use the multihyphonthesis (MH) prediction method based on ridge regression to generate the 3D spatialfeature array from the HSI. Then, we apply the nuclear norm based 2D PCA to multi-view slices (the image with the spatial width and spectral dimension or with the spatial height and spectral dimension) of the former feature array, which can provide a structured spatial-spectral characterization for the reconstruction error slice and further extract the spatialspectral feature. Finally, the 3D spatial-spectral feature array is used to represent the HSI for classification by extreme learning machine (ELM) based on Radial Basis Function (RBF) kernal. Finally, majority voting procedure is used to further improve the classification accuracy. The efficiency of the proposed method is demonstrated by experimental results with real hyperspectral dataset.