Dimensionality reduction of hyperspectral data using spectral fractal feature |
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Authors: | Kriti Mukherjee Jayanta Kumar Ghosh Ramesh Chand Mittal |
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Affiliation: | 1. Civil Engineering Department , Indian Institute of Technology Roorkee , Roorkee , 247667 , India mukherjee.kriti@gmail.com;3. Civil Engineering Department , Indian Institute of Technology Roorkee , Roorkee , 247667 , India;4. Mathematics Department , Indian Institute of Technology Roorkee , Roorkee , 247667 , India |
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Abstract: | A new approach for dimensionality reduction of hyperspectral data has been proposed in this article. The method is based on extraction of fractal-based features from the hyperspectral data. The features have been generated using spectral fractal dimension of the spectral response curves (SRCs) after smoothing, interpolating and segmenting the curves. The new features so generated have then been used to classify hyperspectral data. Comparing the post classification accuracies with some other conventional dimensionality reduction methods, it has been found that the proposed method, with less computational complexity than the conventional methods, is able to provide classification accuracy statistically equivalent to those from conventional methods. |
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Keywords: | hyperspectral data fractal dimension spectral response curve power spectrum |
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