The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Clustering-Based Hyperspectral Band Selection Using Information Measures
Abstract
Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information measures, such as distances based on mutual information or Kullback–Leibler divergence, in order to reduce data redundancy and nonuseful information among image bands. Experimental results include a comparison among some relevant and recent methods for hyperspectral band selection using no labeled information, showing their performance with regard to pixel image classification tasks. The technique that is presented has a stable behavior for different image data sets and a noticeable accuracy, mainly when selecting small sets of bands.
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Cites
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Cited by
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
Tarabalka, Y., Benediktsson, J. A., & Chanussot, J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2016214 · Cited by 678
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing
Jia, S., & Qian, Y. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2002882 · Cited by 369
Metadata
- Authors
- MartÍnez-UsÓMartinez-Uso, A., Pla, F., Sotoca, J. M., & GarcÍa-Sevilla, P.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2007-12
- Volume
- 45
- Issue
- 12
- Pages
- 4158-4171
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2150566919
- Cited by
- 532
- References
- 27
- FWCI
- 25.25
- Type
- journal article
- Language
- en
- Open access
- closed
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