Least squares subspace projection approach to mixed pixel classification for hyperspectral images

@article{Chang1998LeastSS,
  title={Least squares subspace projection approach to mixed pixel classification for hyperspectral images},
  author={Cheng-I Chang and Xiao-Li Zhao and Mark L. G. Althouse and Jeng-Jong Pan},
  journal={IEEE Trans. Geoscience and Remote Sensing},
  year={1998},
  volume={36},
  pages={898-912}
}
An orthogonal subspace projection (OSP) method using linear mixture modeling was recently explored in hyperspectral image classification and has shown promise in signature detection, discrimination, and classification. In this paper, the OSP is revisited and extended by three unconstrained least squares subspace projection approaches, called signature space OSP, target signature space OSP, and oblique subspace projection, where the abundances of spectral signatures are not known a priori but… CONTINUE READING
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