NDLI: Semi-supervised hyperspectral band selection via sparse linear regression and hypergraph models

Content Provider	IEEE Xplore Digital Library
Author	Zhouxiao Guo Haichuan Yang Xiao Bai Zhihong Zhang Jun Zhou
Copyright Year	2013
Description	Author affiliation: Xiamen Univ., Xiamen, China (Zhihong Zhang) \|\| Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China (Zhouxiao Guo; Haichuan Yang; Xiao Bai) \|\| Sch. of Inf. & Commun. Technol., Griffith Univ., Nathan, QLD, Australia (Jun Zhou)
Abstract	Band selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both labeled and unlabeled samples. These coefficients are then used to compute a contribution score for each band, which allows bands with high scores being selected for the testing step. During this process, unlabeled samples also contribute to the coefficients calculation. In order to propagate the labels to these samples, a hypergraph is first built to describe the relationship between labeled and unlabeled samples. This leads to an adjacency matrix whose entries are the sum of corresponding weights of hyperedges. Then matrix subspace learning method is used to estimate the labels of unlabeled samples. The proposed method is evaluated on the APHI dataset. Comparison with several baseline methods has shown the advantages of the proposed method on the pixel-level classification.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1474
Ending Page	1477
File Size	220684
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723064
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Linear regression Computational modeling Semisupervised learning Accuracy Educational institutions hypergraph semi-supervised learning band selection hyperspectral image sparse linear regression
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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