Wavelet-Based Nearest-Regularized Subspace for Noise-Robust Hyperspectral Image Classification

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Wavelet-Based Nearest-Regularized Subspace for Noise-Robust Hyperspectral Image Classification

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Title: Wavelet-Based Nearest-Regularized Subspace for Noise-Robust Hyperspectral Image Classification
Author(s):
Li, Wei;
Liu, Kui;
Su, Hongjun
Item Type: article
Keywords: Redundant discrete wavelet transformation (RDWT)
Nearest regularized subspace (NRS)
Hyperspectral images
Wavelets
Abstract: 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 = 36.87 dB), while the wavelet-based classifier can obtain an accuracy of 71.60%, resulting in an improvement of approximately 6%.
Publisher: SPIE-Society of Photo-Optical Instrumentation Engineers
ISSN: 1931-3195
Persistent Link: http://dx.doi.org/10.1117/1.JRS.8.083665
http://hdl.handle.net/10735.1/4012
Terms of Use: One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Sponsors: National Natural Science Foundation of China (61302164 and 41201341)

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