Hyperspectral image classification using nearest regularized subspace with Manhattan distance

Nearest regularized subspace (NRS) has been recently proposed for hyperspectral image (HSI) classification. The NRS outperforms both collaborative representation classification and sparse representation-based techniques because the NRS makes use of the distance-weighted Tikhonov regularization to ensure appropriate representation from similar samples within-class. However, typical NRS only considers Euclidean distance, which may be suboptimal to resolve the problem of sensitivity in the absolute magnitude of a spectrum. An NRS-Manhattan distance (MD) strategy is proposed for HSI classification. The proposed distance metric controls over magnitude change and emphasizes the shape of the spectrum. Furthermore, the MD metric uses the entire information of the spectral bands in full dimensionality of the HSI pixels, which makes NRS-MD a more efficient pixelwise classifier. Validations are done with several hyperspectral data, i.e., Indian Pines, Botswana, Salinas, and Houston. Results demonstrate that the proposed NRS-MD is superior to other state-of-the-art methods.