Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization
Joint Authors
Zhou, Ruofei
Wang, Gang
Li, Bo
Wang, Jinlong
Liu, Tianzhu
Liu, Chungang
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-14
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
Thanks to the rapid development of hyperspectral sensors, hyperspectral videos (HSV) can now be collected with high temporal and spectral resolutions and utilized to handle invisible dynamic monitoring missions, such as chemical gas plume tracking.
However, using such sequential large-scale data effectively is challenged, because the direct process of these data requires huge demands in terms of computational loads and memory.
This paper presents a key-frame and target-detecting algorithm based on cumulative tensor CANDECOMP/PARAFAC (CP) factorization (CTCF) to select the frames where the target shows up, and a novel super-resolution (SR) method using sparse-based tensor Tucker factorization (STTF) is used to improve the spatial resolution.
In the CTCF method, the HSV sequence is seen as cumulative tensors and the correlation of adjacent frames is exploited by applying CP tensor approximation.
In the proposed STTF-based SR method, we consider the HSV frame as a third-order tensor; then, HSV frame super-resolution problem is transformed into estimations of the dictionaries along three dimensions and estimation of the core tensor.
In order to promote sparse core tensors, a regularizer is incorporated to model the high spatial-spectral correlations.
The estimations of the core tensor and the dictionaries along three dimensions are formulated as sparse-based Tucker factorizations of each HSV frame.
Experimental results on real HSV data set demonstrate the superiority of the proposed CTCF and STTF algorithms over the comparative state-of-the-art target detection and SR approaches.
American Psychological Association (APA)
Zhou, Ruofei& Wang, Gang& Li, Bo& Wang, Jinlong& Liu, Tianzhu& Liu, Chungang. 2020. Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1202330
Modern Language Association (MLA)
Zhou, Ruofei…[et al.]. Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization. Mathematical Problems in Engineering No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1202330
American Medical Association (AMA)
Zhou, Ruofei& Wang, Gang& Li, Bo& Wang, Jinlong& Liu, Tianzhu& Liu, Chungang. Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1202330
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202330