Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 945-948, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/945/2016/
doi:10.5194/isprs-archives-XLI-B3-945-2016
 
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 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.
Conference paper (PDF, 735 KB)


Citation: Zhang, H., Zhai, H., Liao, W., Cao, L., Zhang, L., and Pižurica, A.: HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 945-948, doi:10.5194/isprs-archives-XLI-B3-945-2016, 2016.

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