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Quartimax Rotational Principal Component Analysis for Land Use and Land Cover Classification

Author Affiliations

  • 1 Department of Remote Sensing, BIT Mesra, Ranchi, Jharkhand-835215, India

Int. Res. J. Earth Sci., Volume 4, Issue (4), Pages 9-16, April,25 (2016)

Abstract

LULC classification were performed using Rotational Principal component approach on multispectral Landsat8 OLI datasets to increase the spectral divergence among the classes, which result better classification accuracy. We adopted Quartimax Rotational criteriato perform rotation using PC layers, which were obtained by performing PCA transformation using multispectral bands. We observed that, Quartimax rotational criteria improved the level of classificationaccuracy by enhancing the spectral characteristics of the different spectral land cover classand satisfied higher classification accuracy than an ordinary PCA transformation approach over the same multispectral dataset.

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