The DOI matches its Crossref record; 182 of the 182 references it cites were found in a registry or an index.
Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review
Abstract
Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a more comprehensive characterization of the Earth’s surface. To better exploit HSIs, a large number of algorithms have been developed during the past few decades. Due to their very high correlation between spectral channels and spatial pixels, HSIs have intrinsically sparse and low-rank structures. The sparse representation (SR) and low-rank representation (LRR)-based methods have proven to be powerful tools for HSI processing and are widely used in different HS fields. In this article, we present a survey of low-rank and sparse-based HSI processing methods in the fields of denoising, superresolution, dimension reduction, unmixing, classification, and anomaly detection. The purpose is to provide guidelines and inspiration to practitioners for promoting the development of HSI processing. For a listing of the key terms discussed in this article, see “Nomenclature.”
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Cites
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
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Robust Face Recognition via Sparse Representation
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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321 · cited 6×
“In [199], an HSI is decomposed into a dictionary and its corresponding coefficient matrix by the K-singular value decomposition (K-SVD).”
BackgroundMethodInfluentialRobust Recovery of Subspace Structures by Low-Rank Representation
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Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion
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Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2129595 · Cited by 1,155
Composite Kernels for Hyperspectral Image Classification
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Sparse Unmixing of Hyperspectral Data
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Cited by
SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers
Hong, D., Han, Z., Yao, J., Gao, L., Zhang, B., Plaza, A., & Chanussot, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3130716 · Cited by 1,382
Zhao, C., Qin, B., Feng, S., Zhu, W., Zhang, L., & Ren, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3230378 · Cited by 294
Hyperspectral anomaly detection with robust graph autoencoders
Fan, G., Ma, Y., Mei, X., Fan, F., Huang, J., & Ma, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3097097 · Cited by 212
Chen, J., Zhao, M., Wang, X., Richard, C., & Rahardja, S. (2023) · IEEE Signal Processing Magazine · doi:10.1109/msp.2022.3208987 · Cited by 90
Metadata
- Authors
- Peng, J., Sun, W., Li, H.-C., Li, W., Meng, X., Ge, C., & Du, Q.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2022-03
- Volume
- 10
- Issue
- 1
- Pages
- 10-43
- ISSN
- 2168-6831 (ISSN-L)
- 2473-2397
- 2373-7468
- OpenAlex
- W3168931281
- Cited by
- 285
- References
- 182
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Peng, J., Sun, W., Li, H.-C., Li, W., Meng, X., Ge, C., & Du, Q. (2022). Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review. IEEE Geoscience and Remote Sensing Magazine. https://doi.org/10.1109/mgrs.2021.3075491
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