A wavelet-based nearest-regularized-subspace classifier is proposed for noise-robust
hyperspectral image (HSI) classification. The nearest-regularized subspace, coupling the nearest-
subspace classification with a distance-weighted Tikhonov regularization, was designed to
only consider the original spectral bands. Recent research found that the multiscale wavelet features
[e.g., extracted by redundant discrete wavelet transformation (RDWT)] of each hyperspectral
pixel are potentially very useful and less sensitive to noise. An integration of wavelet-based
features and the nearest-regularized-subspace classifier to improve the classification performance
in noisy environments is proposed. Specifically, wealthy noise-robust features provided
by RDWT based on hyperspectral spectrum are employed in a decision-fusion system or as
preprocessing for the nearest-regularized-subspace (NRS) classifier. Improved performance
of the proposed method over the conventional approaches, such as support vector machine,
is shown by testing several HSIs. For example, the NRS classifier performed with an accuracy
of 65.38% for the AVIRIS Indian Pines data with 75 training samples per class under noisy
conditions (signal-to-noise ratio ¼ 36.87 dB), while the wavelet-based classifier can obtain
an accuracy of 71.60%, resulting in an improvement of approximately 6%.
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