Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data

@article{Plaza2011ForewordTT,
  title={Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data},
  author={Antonio J. Plaza and Qian Du and Jos{\'e} M. Bioucas-Dias and Xiuping Jia and Fred A. Kruse},
  journal={IEEE Trans. Geosci. Remote. Sens.},
  year={2011},
  volume={49},
  pages={4104-4111}
}
  • A. PlazaQ. Du F. Kruse
  • Published 13 October 2011
  • Environmental Science, Mathematics
  • IEEE Trans. Geosci. Remote. Sens.
The 19 papers in this special issue focus on the state-of-the-art and most recent developments in the area of spectral unmixing of remotely sensed data. 
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References

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A new algorithm - iterated constrained endmembers (ICE) - which attempts to address these shortcomings is introduced and an example of its use is given.

Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study

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Spatial Preprocessing for Endmember Extraction

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The experimental results indicate that the spectral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model.

Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping

This study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data and results indicate that the described unmixing approach yields moderate results for the 12-class case and good resultsFor the 4-class cases, and results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability.

A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation

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