Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 21, Issue -, Pages 535-544Publisher
ELSEVIER
DOI: 10.1016/j.jag.2012.07.011
Keywords
Spectral resampling; Inter-band correlation; Grass species classification; Random forests
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Funding
- National Research Foundation (NRF)
- KwaZulu-Natal Department of Agriculture and Environmental Affairs (KZNDAE)
- Ezemvelo KZN Wildlife
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In this paper, a user-defined inter-band correlation filter function was used to resample hyperspectral data and thereby mitigate the problem of multicollinearity in classification analysis. The proposed resampiing technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. Weighting threshold of inter-band correlation (WTC, Pearson's r) was calculated, whereby r = 1 at the band-centre. Various WTC (r = 0.99, r = 0.95 and r = 0.90) were assessed, and bands with coefficients beyond a chosen threshold were assigned r = 0. The resultant data were used in the random forest analysis to classify in situ C-3 and C-4 grass canopy reflectance. The respective WTC datasets yielded improved classification accuracies (kappa = 0.82, 0.79 and 0.76) with less correlated wavebands when compared to resampled Hyperion bands (kappa = 0.76). Overall, the results obtained from this study suggested that resampling of hyperspectral data should account for the spectral dependence information to improve overall classification accuracy as well as reducing the problem of multicollinearity. (C) 2012 Elsevier B.V. All rights reserved.
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