Abstract
Capturing images in thousands of contiguous spectral bands has been made simpler with the emergence of technology in the field of hyperspectral remote sensing. Despite of these huge data available for analysis, Hyperspectral images (HSI) face many challenges due to high dimensionality, noise, spectral mixing and computational complexity. Several preprocessing methods can be used to overcome the above mentioned issues. In this paper, an enhancement technique using 2D-Empirical Wavelet Transform (EWT) is used as a preprocessing step for the HSI reconstruction prior to sparsity based classification (Subspace Pursuit and Orthogonal Matching Pursuit). The effectiveness of the proposed method is proved by comparing the classification results obtained with and without applying preprocessing. Experimental analysis shows a significant improvement in the classification accuracies i.e., for 40\(\%\) of training samples, OMP shows an improvement in overall classification accuracy from 66.12\(\%\) to 93.20\(\%\) and SP shows an improvement from 66.36\(\%\) to 92.74\(\%\).
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Nidhin Prabhakar, T.V., Geetha, P. (2016). Empirical Wavelet Transform for Improved Hyperspectral Image Classification. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_34
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DOI: https://doi.org/10.1007/978-3-319-23036-8_34
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