The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
Abstract
A new spectral-spatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.
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Cites
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Cited by
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationDeep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Kang, X., Li, S., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2264508 · Cited by 704
Self-citationHe, L., Li, J., Liu, C., & Li, S. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2765364 · Cited by 630
Metadata
- Authors
- Tarabalka, Y., Benediktsson, J. A., & Chanussot, J.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2009-08
- Volume
- 47
- Issue
- 8
- Pages
- 2973-2987
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2164437025
- Cited by
- 678
- References
- 44
- FWCI
- 68.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tarabalka, Y., Benediktsson, J. A., & Chanussot, J. (2009). Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2009.2016214
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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