• Corpus ID: 207871101

Spatial Sparse subspace clustering for Compressive Spectral imaging

  title={Spatial Sparse subspace clustering for Compressive Spectral imaging},
  author={Jianchen Zhu and Tong Zhang and Shengjie Zhao and Carlos Hinojosa and Zengli Liu and Gonzalo R. Arce},
This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial… 


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A novel spectral-spatial sparse subspace clustering S4C algorithm for hyperspectral remote sensing images is proposed by treating each kind of land-cover class as a subspace and considering the spectral and spatial properties of HSIs.
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2016
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    2012 IEEE Statistical Signal Processing Workshop (SSP)
  • 2012
The Restricted Isometry Property (RIP) for the projection matrices used in CASSI provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction and provides the optimal transmittance parameters for the set of code apertures used in the acquisition process.
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