HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/945/2016/isprs-archives-XLI-B3-945-2016.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |