Hyperspectral classification using an adaptive spectral-spatial kernel-based low-rank approximation

This paper presents a novel adaptive spectral-spatial kernel-based low-rank approximation method for spectral-spatial hyperspectral image (HSI) classification. In the first of three steps of the proposed method, superpixel and image patch are used together to calculate the weights in the homogeneous region. Second, an adaptive spectral-spatial kernel is defined to capture the spectral and spatial feature of HSIs. In the final step, an adaptive spectral-spatial kernel and low-rank approximation are integrated into a decision model to perform HSI classification. Extensive experimental results on Indian Pines and Pavia University demonstrate the superiority of the proposed classifier when compared with other competing classifiers.