UM  > Faculty of Science and Technology  > DEPARTMENT OF MATHEMATICS
Multiple one-dimensional embedding clustering scheme for hyperspectral image classification
Song Y.3; Li H.3; Wang J.2; Kou K.I.1
2016-03-01
Source PublicationInternational Journal of Wavelets, Multiresolution and Information Processing
ISSN02196913
Volume14Issue:2
AbstractIn this paper, we present a novel multiple 1D-embedding based clustering (M1DEBC) scheme for hyperspectral image (HSI) classification. This novel clustering scheme is an iteration algorithm of 1D-embedding based regularization, which is first proposed by J. Wang [Semi-supervised learning using ensembles of multiple 1D-embedding-based label boosting, Int. J. Wavelets, Multiresolut. Inf. Process. 14(2) (2016) 33 pp.; Semi-supervised learning using multiple one-dimensional embedding-based adaptive interpolation, Int. J. Wavelets, Multiresolut. Inf. Process. 14(2) (2016) 11 pp.]. In the algorithm, at each iteration, we do the following three steps. First, we construct a 1D multi-embedding, which contains k different versions of 1D embedding. Each of them is realized by an isometric mapping that maps all the pixels in a HSI into a line such that the sum of the distances of adjacent pixels in the original space is minimized. Second, for each 1D embedding, we use the regularization method to find a pre-classifier to give each unlabeled sample a preliminary label. If all of the k different versions of regularization vote the same preliminary label, then we call it a feasible confident sample. All the feasible confident samples and their corresponding labels constitute the auxiliary set. We randomly select a part of the elements from the auxiliary set to construct the newborn labeled set. Finally, we add the newborn labeled set into the labeled sample set. Thus, the labeled sample set is gradually enlarged in the process of the iteration. The iteration terminates until the updated labeled set reaches a certain size. Our experimental results on real hyperspectral datasets confirm the effectiveness of the proposed scheme.
Keyword1D multi embedding hyperspectral image classification regularization Sorting
DOI10.1142/S021969131640004X
URLView the original
Language英語
Fulltext Access
Citation statistics
Cited Times [WOS]:9   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF MATHEMATICS
Affiliation1.Universidade de Macau
2.Sam Houston State University
3.Huazhong University of Science and Technology
Recommended Citation
GB/T 7714
Song Y.,Li H.,Wang J.,et al. Multiple one-dimensional embedding clustering scheme for hyperspectral image classification[J]. International Journal of Wavelets, Multiresolution and Information Processing,2016,14(2).
APA Song Y.,Li H.,Wang J.,&Kou K.I..(2016).Multiple one-dimensional embedding clustering scheme for hyperspectral image classification.International Journal of Wavelets, Multiresolution and Information Processing,14(2).
MLA Song Y.,et al."Multiple one-dimensional embedding clustering scheme for hyperspectral image classification".International Journal of Wavelets, Multiresolution and Information Processing 14.2(2016).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Song Y.]'s Articles
[Li H.]'s Articles
[Wang J.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Song Y.]'s Articles
[Li H.]'s Articles
[Wang J.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Song Y.]'s Articles
[Li H.]'s Articles
[Wang J.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.