By Topic

Multivariate Gray Model-Based BEMD for Hyperspectral Image Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Zhi He ; Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin, China ; Qiang Wang ; Yi Shen ; Jing Jin
more authors

Bidimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing due to its fully data-driven and self-adaptive nature. Unfortunately, this promising technique is sensitive to boundary effect. In this paper, a new method inspired by the multivariate gray model (MGM), namely GM(1, N), is developed for boundary extension of the BEMD. Specifically, our contribution is threefold. First, focusing on evaluating the model coefficients and convolution integral, which are key elements in reducing the prediction error of the GM(1, N), we replace the existing (composite) trapezoidal rule with (composite) Simpson rule and deduce an alternative MGM, termed as S-GM(1,N). Second, the given image is extended by the GM(1, 3) or S-GM(1, 3) (N=3), whose characteristic data series and relative data series are, respectively, derived from the pixel values and coordinates of the image. Consequently, the extended image is decomposed into several bidimensional intrinsic mode functions (BIMFs) and a residue whose corresponding parts are extracted as the decomposition results of the original image. Finally, the proposed boundary effect mitigation methods are applied in the hyperspectral image classification. In greater detail, the BIMFs obtained by various BEMD methods are taken as features of the hyperspectral dataset whereas the widely used k-nearest neighbors (k -NN) as well as the support vector machine, whose optimal parameters are selected by the genetic algorithm, are adopted as classifiers. Extensive experiments and comparisons with other generally acknowledged methods confirm that the proposed methods achieve promising improvement in the classification performance.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:62 ,  Issue: 5 )