Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection
| dc.contributor.author | Chang, Chein-I | |
| dc.contributor.author | Cao, Hongju | |
| dc.contributor.author | Chen, Shuhan | |
| dc.contributor.author | Shang, Xiaodi | |
| dc.contributor.author | Yu, Chunyan | |
| dc.contributor.author | Song, Meiping | |
| dc.date.accessioned | 2022-11-09T18:02:35Z | |
| dc.date.available | 2022-11-09T18:02:35Z | |
| dc.date.issued | 2020-07-14 | |
| dc.description.abstract | Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSPGoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m + j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors. | en_US |
| dc.description.sponsorship | The work of Chein-I Chang was supported by the Fundamental Research Funds for Central Universities under Grant 3132019341. The work of Hongju Cao was supported by the Nature Science Foundation of Liaoning Province under Grant 20180550018. The work of Meiping Song was supported by the National Nature Science Foundation of China under Grant 61601077, Grant 61971082, and Grant 61890964. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9140357 | en_US |
| dc.format.extent | 27 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | postprints | en_US |
| dc.identifier | doi:10.13016/m2a0fr-c3qq | |
| dc.identifier.citation | C. -I. Chang, H. Cao, S. Chen, X. Shang, C. Yu and M. Song, "Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2403-2429, March 2021, doi: 10.1109/TGRS.2020.3002724. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/TGRS.2020.3002724 | |
| dc.identifier.uri | http://hdl.handle.net/11603/26282 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
| dc.title | Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5450-4891 | en_US |
| dcterms.creator | https://orcid.org/0000-0001-9996-0666 | en_US |