Abstract
Many remote sensing instruments record more channels or bands of data than are actually needed for most applications. As an example, even though the Hyperion sensor on EO-1 produces 220 channels of image data over the wavelength range 0.4–2.4 μm, it is unlikely that channels beyond about 1.0 μm would be relevant for water studies, unless the water were especially turbid. Furthermore, unless the actual reflectance spectrum of the water was essential for the task at hand, it may not even be necessary to use all the contiguous bands recorded in the range 0.4–1.0 μm; instead, a representative subset may be sufficient in most cases.
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Notes
- 1.
P.H. Swain and S.M. Davis, eds., Remote Sensing: the Quantitative Approach, McGraw-Hill, NY., 1978.
- 2.
See Swain and Davis, loc. cit.
- 3.
See A.G. Wacker, The Minimum Distance Approach to Classification, PhD Thesis, Purdue University, West Lafayette, Indiana, 1971.
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See T. Kailath, The divergence and Bhattacharyya distance measures in signal selection, IEEE Transactions on Communications Theory, vol. COM-15, 1967, pp. 52–60.
- 5.
See Swain and Davis, loc. cit.
- 6.
See P.H Swain and R.C. King, Two effective feature selection criteria for multispectral remote sensing, Proceedings 1st International Joint Conference on Pattern Recognition, November 1973, pp. 536–540, and P.W. Mausel, W.J. Kramber and J.K. Lee, Optimum band selection for supervised classification of multispectral data, Photogrammetric Engineering and Remote Sensing, vol. 56, 1990, pp. 55–60.
- 7.
Kailath, loc. cit.
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See H. Seal, Multivariate Statistical Analysis for Biologists, Methuen, London, 1964, and N.A. Campbell and W.R. Atchley, The geometry of canonical variate analysis, Systematic Zoology, vol. 30, 1981, pp. 268–280.
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See K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic, London, 1990.
- 10.
If a matrix is equivalent to the identity matrix then so is its inverse. Further \( [{\mathbf{ABC}}]^{ - 1} = {\mathbf{C}}^{ - 1} {\mathbf{B}}^{ - 1} {\mathbf{A}}^{ - 1} . \)
- 11.
See Fukunaga, loc. cit.
- 12.
Fukunaga, ibid.
- 13.
See B–C Kuo and D.A. Landgrebe, Nonparametric weighted feature extraction for classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, May 2004, pp. 1096–1105.
- 14.
See Sect. 6.6 of D.A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons, Hoboken N.J., 2003.
- 15.
See Kuo and Landgrebe, loc. cit.
- 16.
See X. Jia, Classification Techniques for Hyperspectral Remote Sensing Image Data, PhD Thesis, The University of New South Wales, University College, Australian Defence Force Academy, Canberra, 1996, and X. Jia and J.A. Richards Efficient maximum likelihood classification for imaging spectrometer data sets, IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 2, March 1994, pp. 274–281.
- 17.
Jia, loc. cit.
- 18.
X. Jia and J.A. Richards, Segmented principal components transformation for efficient hyperspectral remote sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 1 pt. 2, 1999, pp. 538–542.
- 19.
See Jia, loc. cit.
- 20.
See Landgrebe, 2003, loc. cit.
- 21.
Many of the historical information notes and reports produced by the Laboratory for Applications of Remote Sensing (LARS) at Purdue University have been scanned and are available at http://www.lars.purdue.edu/home/References.html
- 22.
See Kailath, loc. cit.
- 23.
See P.H. Swain and R.C. King, Two effective feature selection criteria for multispectral remote sensing, Proc. First Int. Joint Conf. on Pattern Recognition, November 1973, pp. 536–540.
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Richards, J.A. (2013). Feature Reduction. In: Remote Sensing Digital Image Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30062-2_10
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