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#4523. Featured clustering and ranking-based bad cluster removal for hyperspectral band selection and classification using ensemble of binary SVM classifiers
| August 2026 | publication date |
| Proposal available till | 16-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Mathematics |
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Abstract:
The rich spectral and spatial information of hyperspectral images are well known in the literature. The higher dimensionality of HSI creates Hughess effect and increased computational complexity. This demands reduction for HS images as a pre-processing step. The necessary reduction of bands can be achieved by a proper band selection (BS) technique. The proposed features based unsupervised BS technique follows three subsequent steps: 1) for each band image statistical features are extracted, 2) bands are clustered with a k-means approach using the extracted features, 3) each cluster is ranked using mean entropy measure, 4) bad clusters are removed, and 5) for each selected cluster, a representative band is selected. The proposed method is validated over three widely used standard datasets and six state-of-the-art approaches using an ensemble of binary SVM classifiers. The obtained results strongly suggest the clustering is essential to reduce the redundancy, and removal of cluster is informative to keep the informative bands.
Keywords:
Band Selection; Cluster Removal; Clustering; Ensemble Algorithm; Feature; Hyperspectral Image; K-Means; Ranking; Unsupervised
Contacts :
2 place - free (for sale)
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4 place - free (for sale)
Abstract:
The rich spectral and spatial information of hyperspectral images are well known in the literature. The higher dimensionality of HSI creates Hughess effect and increased computational complexity. This demands reduction for HS images as a pre-processing step. The necessary reduction of bands can be achieved by a proper band selection (BS) technique. The proposed features based unsupervised BS technique follows three subsequent steps: 1) for each band image statistical features are extracted, 2) bands are clustered with a k-means approach using the extracted features, 3) each cluster is ranked using mean entropy measure, 4) bad clusters are removed, and 5) for each selected cluster, a representative band is selected. The proposed method is validated over three widely used standard datasets and six state-of-the-art approaches using an ensemble of binary SVM classifiers. The obtained results strongly suggest the clustering is essential to reduce the redundancy, and removal of cluster is informative to keep the informative bands.
Keywords:
Band Selection; Cluster Removal; Clustering; Ensemble Algorithm; Feature; Hyperspectral Image; K-Means; Ranking; Unsupervised
Contacts :
