Conference Paper

J. E. Fowler, Q. Du, W. Zhu, and N. H. Younan, “Classification Performance of Random-Projection-Based Dimensionality Reduction of Hyperspectral Imagery,” in Proceedings of the International Geoscience and Remote Sensing Symposium, Capetown, South Africa, July 2009, vol. 5, pp. 76-79.

• Abstract:
  High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensionality reduction on-board remote devices is often prohibitive due to limited computational resources; on the other hand, integrating random projections directly into signal acquisition offers alternative dimensionality reduction without sender-side computational cost. Effective receiver-side reconstruction from such random projections has been demonstrated previously using compressive-projection principal component analysis (CPPCA). While this prior work has focused on squared-error quality measures, the present work reports experimental results illustrating preservation of statistical class separation and anomaly-detection performance for CPPCA reconstruction following random-projection-based dimensionality reduction.

• Text:

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