Conference Paper

Q. Du and J. E. Fowler, “On the Performance of Random-Projection-Based Dimensionality Reduction for Endmember Extraction,” in Proceedings of the International Geoscience and Remote Sensing Symposium, Honolulu, HI, July 2010, pp. 1277-1280.

• Abstract:
  In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction is capable of providing better endmembers after effective decision fusion.

• Text:

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