Semi-supervised feature selection with sparse representation for hyperspectral image classification Online publication date: Mon, 27-Nov-2017
by Yanyan Zhang; Shiguo Chen; Cailing Wang; Zhisong Pan; Daoqiang Zhang
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 2, No. 1, 2017
Abstract: Dimensionality reduction is one of the most important steps for remotely sensed hyperspectral image classification. Feature selection as a kind of dimensionality reduction has attracted great attentions in the recent decades. In this paper, we proposed a novel feature selection method for hyperspectral image classification based on semi-supervised learning and sparsity score (or briefly called semi-supervised sparsity score (semi-SS)). In semi-SS, the pairwise constraints instead of class labels are used as the supervision information. Furthermore, the features chosen by Semi-SS have the ability to reconstruct the original data and sparsity preserving. Experiments conducted on two famous hyperspectral data sets illustrate that the proposed algorithm is remarkably effective in comparison to the existing feature selection methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com