Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data
Joint Authors
Kong, Fanqiang
Guo, Wenjun
Li, Yunsong
Shen, Qiu
Liu, Xin
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-05-14
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library.
Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library.
But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting endmembers will have a negative effect on the estimation of the abundances corresponding to the actual endmembers.
This paper presents a variant of SOMP, termed backtracking-based SOMP (BSOMP), for sparse unmixing of hyperspectral data.
As an extension of SOMP, BSOMP incorporates a backtracking technique to detect the previous chosen endmembers’ reliability and then deletes the unreliable endmembers.
Through this modification, BSOMP can select the true endmembers more accurately than SOMP.
Experimental results on both simulated and real data demonstrate the effectiveness of the proposed algorithm.
American Psychological Association (APA)
Kong, Fanqiang& Guo, Wenjun& Li, Yunsong& Shen, Qiu& Liu, Xin. 2015. Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1074871
Modern Language Association (MLA)
Kong, Fanqiang…[et al.]. Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-17.
https://search.emarefa.net/detail/BIM-1074871
American Medical Association (AMA)
Kong, Fanqiang& Guo, Wenjun& Li, Yunsong& Shen, Qiu& Liu, Xin. Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-17.
https://search.emarefa.net/detail/BIM-1074871
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074871