Hyperspectral image kernel sparse subspace clustering with spatial max pooling operation

Hongyan Zhang, Han Zhai, Wenzhi Liao, Liqin Cao, Liangpei Zhang, Aleksandra Pizurica, George Vosselman (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

4 Citations (Scopus)


In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, by mapping the feature points into a higher dimensional space from the original space with the kernel strategy, 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.
Original languageEnglish
Title of host publicationXXIII ISPRS Congress, Commission III
Subtitle of host publicationInternational Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences
Number of pages4
Publication statusPublished - 2016
Event23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) - Prague, Czech Republic
Duration: 11 Jul 201619 Jul 2016


Conference23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)
Abbreviated titleISPRS2016
Country/TerritoryCzech Republic


  • hyperspectral image
  • spatial max pooling
  • nonlinear
  • kernel
  • SSC


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