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Kernel methods, e.g., composite kernels (CKs) and spatial-spectral kernels (SSKs), have been demonstrated to be an effective way to exploit the spatial-spectral information nonlinearly for improving the classification performance of hyperspectral image (HSI). However, these methods are always conducted with square-shaped window or superpixel techniques. Both techniques are likely to misclassify the pixels that lie at the boundaries of class, and thus a small target is always smoothed away. To alleviate these problems, in this paper, we propose a novel patch-based low rank component induced spatial-spectral kernel method, termed LRCISSK, for HSI classification. First, the latent low-rank features of spectra in each cubic patch of HSI are reconstructed by a low rank matrix recovery (LRMR) technique, and then, to further explore more accurate spatial information, they are used to identify a homogeneous …