The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
Abstract
Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches.
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Cites
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Cited by
Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
Self-citationRegularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
Dian, R., Li, S., & Kang, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2980398 · Cited by 328
Self-citationSSR-NET: Spatial–Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion
Zhang, X., Huang, W., Wang, Q., & Li, X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3018732 · Cited by 292
Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review
Peng, J., Sun, W., Li, H.-C., Li, W., Meng, X., Ge, C., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3075491 · Cited by 285
MHF-Net: An Interpretable Deep Network for Multispectral and Hyperspectral Image Fusion
Xie, Q., Zhou, M., Zhao, Q., Xu, Z., & Meng, D. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3015691 · Cited by 268
Metadata
- Authors
- Li, S., Dian, R., Fang, L., & Bioucas-Dias, J. M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-08
- Volume
- 27
- Issue
- 8
- Pages
- 4118-4130
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2804744787
- Cited by
- 585
- References
- 56
- FWCI
- 34.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, S., Dian, R., Fang, L., & Bioucas-Dias, J. M. (2018). Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2018.2836307
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