The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms
Abstract
Hyperspectral imaging applications are many and span civil, environmental, and military needs. Typical examples include the detection of specific terrain features and vegetation, mineral, or soil types for resource management; detecting and characterizing materials, surfaces, or paints; the detection of man-made materials in natural backgrounds for the purpose of search and rescue; the detection of specific plant species for the purposes of counter narcotics; and the detection of military vehicles for the purpose of defense and intelligence. The objective of this article is to provide a tutorial overview of detection algorithms used in current hyperspectral imaging systems that operate in the reflective part of the spectrum (0.4 - 24 μm.) The same algorithms might be used in the long-wave infrared spectrum; however, the phenomenology is quite different. The covered topics and the presentation style have been chosen to illustrate the strong couplings among the underlying phenomenology, the theoretical framework for algorithm development and analysis, and the requirements of practical applications.
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Citations
Cites
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Muirhead, R. J. (1982) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316559 · Cited by 2,260
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Li, J., Stoica, P., & Wang, Z. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2003.812831 · Cited by 1,439
Signal Theory Methods in Multispectral Remote Sensing
Landgrebe, D. A. (2003) · Wiley eBooks · doi:10.1002/0471723800 · Cited by 1,205
Detection algorithms for hyperspectral imaging applications
Manolakis, D., & Shaw, G. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974724 · Cited by 1,116
Self-citationRobust minimum variance beamforming
Lorenz, R. G., & Boyd, S. P. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.845436 · Cited by 774
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Anomaly detection from hyperspectral imagery
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Cited by
Hyperspectral Target Detection : An Overview of Current and Future Challenges
Nasrabadi, N. M. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2278992 · Cited by 580
Shimoni, M., Haelterman, R., & Perneel, C. (2019) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2902525 · Cited by 537
Jiang, T., Li, Y., Xie, W., & Du, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2965961 · Cited by 209
Siamese transformer network for hyperspectral image target detection
Rao, W., Gao, L., Qu, Y., Sun, X., Zhang, B., & Chanussot, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3163173 · Cited by 134
Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data
Scafutto, R. D. P. M., & De Souza Filho, C. R. (2018) · Remote Sensing · doi:10.3390/rs10081237 · Cited by 27
“, HC based plastic, paint, alphalt) or rare materials not related to the target occur in the image [33].”
Background
Metadata
- Authors
- Manolakis, D., Truslow, E., Pieper, M., Cooley, T., & Brueggeman, M.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2014-01
- Volume
- 31
- Issue
- 1
- Pages
- 24-33
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W1972578813
- Cited by
- 279
- References
- 21
- FWCI
- 27.84
- Type
- journal article
- Language
- en
- Open access
- closed
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