The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection
Open accessAbstract
Hyperspectral imaging is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light intensity variation across a large number of spectral bands or wavelengths; alternatively, they can be thought of as a measurement of the spectrum of light transmitted or reflected from each spatial location in a scene. Because chemical elements have unique spectral signatures, observing the spectra at a high spatial and spectral resolution provides information about the material properties of the scene with much more accuracy than is possible with conventional three-color images. As a result, hyperspectral imaging is used in a variety of important applications, including remote sensing, astronomical imaging, and fluorescence microscopy.
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Cites
High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
Loh, P.-L., & Wainwright, M. J. (2012) · The Annals of Statistics · doi:10.1214/12-aos1018 · Cited by 529
Cited by
Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder
Ma, X., Wang, H., & Geng, J. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2517204 · Cited by 258
Metadata
- Authors
- Willett, R. M., Duarte, M. F., Davenport, M. A., & Baraniuk, R. G.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2014-01
- Volume
- 31
- Issue
- 1
- Pages
- 116-126
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2049189005
- Cited by
- 200
- References
- 1
- FWCI
- 36.26
- Type
- journal article
- Language
- en
- Open access
- green
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- Willett, R. M., Duarte, M. F., Davenport, M. A., & Baraniuk, R. G. (2014). Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2013.2279507
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