Research Papers

Wavelet-based nearest-regularized subspace for noise-robust hyperspectral image classification

[+] Author Affiliations
Wei Li

Beijing University of Chemical Technology, College of Information Science and Technology, Beijing 100029, China

Kui Liu

University of Texas at Dallas, Department of Electrical Engineering, Richardson, Texas 75080

Hongjun Su

Hohai University, School of Earth Sciences and Engineering, Nanjing 210098, China

J. Appl. Remote Sens. 8(1), 083665 (Mar 17, 2014). doi:10.1117/1.JRS.8.083665
History: Received August 29, 2013; Revised February 12, 2014; Accepted February 18, 2014
Text Size: A A A

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

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Wei Li ; Kui Liu and Hongjun Su
"Wavelet-based nearest-regularized subspace for noise-robust hyperspectral image classification", J. Appl. Remote Sens. 8(1), 083665 (Mar 17, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083665


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification. IEEE Trans Image Process Published online Jan 18, 2017;
3-D Solid Texture Classification Using Locally-Oriented Wavelet Transforms. IEEE Trans Image Process Published online Feb 06, 2017;
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.