Hyperspectral Image Processing Using Locally Linear Embedding

@inproceedings{Kim2016HyperspectralIP,
  title={Hyperspectral Image Processing Using Locally Linear Embedding},
  author={David H. Kim and Leif H. Finkel},
  year={2016}
}
We describe a method of processing hyperspectral images of natural scenes that uses a combination of kmeans clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorithm. Representative pixels from each cluster are chosen and passed to the LLE algorithm, where the high… CONTINUE READING
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References

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Estimation of crop cover and chlorophyll from hyperspectral remote sensing

  • J.-C. Deguise H. McNaim, A. Pacheco, J. Shang, N. Rabe
  • 2001

Finding new mineral prospects with HYMAP : early results from a hyperspectral remote - sensing case study in west Pilbara

  • P. Bierwirth, R. Blewett, D. Huston
  • ASGO Research Newsletter
  • 1999

The use of hyperspectral data for precision farming

  • J.-C Deguise K. Staenz, J. Chen, H. McNaim, T. Szeredi, M. McGovem

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