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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Cloud Implementation of a Full Hyperspectral Unmixing Chain Within the NASA Web Coverage Processing Service for EO-1

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References

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This book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.

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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

Comparisons show that misregistration inaccuracies between the HiRISE and CRISM images represent the major source of error, and abundance maps provided by three methods out of seven are generally accurate, i.e., sufficient for a planetary interpretation.

Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing

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A novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information, and can be combined with any spectral-based endmember extraction technique.

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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.
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