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In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm forhyperspectral remote sensing imagery. Firstly, by mapping the feature points into a higher dimensional space from the original spacewith the kernel strategy, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore thecomplex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix.Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clusteringresult. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperformsthe state-of-the-art clustering methods

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION