Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

  title={Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising},
  author={Yuan Yuan and Xiangtao Zheng and Xiaoqiang Lu},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral-spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained… CONTINUE READING
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Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis

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