Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 945-948, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-945-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 945-948, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-945-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jun 2016

10 Jun 2016

HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION

Hongyan Zhang1,2, Han Zhai2, Wenzhi Liao1, Liqin Cao3, Liangpei Zhang2, and Aleksandra Pižurica1 Hongyan Zhang et al.
  • 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.