HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
- 1Ghent University, Dept. Telecommunications and Information Processing, TELIN-IPI-iMinds, Ghent, Belgium
- 2The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, China
- 3School of Printing and Packaging, Wuhan University, China
Keywords: Hyperspectral image, nonlinear processing, spatial max pooling, SSC, kernel
Abstract. In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex 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 clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.