Share Email Print

Journal of Applied Remote Sensing

Wavelet-based nearest-regularized subspace for noise-robust hyperspectral image classification
Author(s): Wei Li; Kui Liu; Hongjun Su
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper 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 ¼ 36.87 dB), while the wavelet-based classifier can obtain an accuracy of 71.60%, resulting in an improvement of approximately 6%.

Paper Details

PDF: 14 pages
J. Appl. Remote Sens. 8(1) 083665 doi: 10.1117/1.JRS.8.083665
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1, January 2014
Show Author Affiliations
Wei Li, Beijing Univ. of Chemical Technology (China)
Kui Liu, The Univ. of Texas at Dallas (United States)
Hongjun Su, Hohai Univ. (China)

© SPIE. Terms of Use
Back to Top