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Band Selection Using Dilation Distances
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DOI：10.1109/LGRS.2021.3057117.png)
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Abstract
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In this letter, we adapt the dilation operator from mathematical morphology to propose dilation distances. These dilation distances are then used for band selection in hyperspectral images. It is shown that dilation distances between bands can capture the spatial distance between the objects. Hence, using dilation-based distances would select those bands which identify spatially separated objects. This is illustrated using both toy and real data sets. Furthermore, we compare the proposed approach with existing methods and show empirically that dilation-distance-based band selection provided competitive results outperforming several methods.
Keywords:
Gray-scale
Correlation
Feature extraction
Complexity theory
Morphology
Computer science
Toy manufacturing industry
Band selection
dilation
feature selection
hyperspectral images
mathematical morphology (MM)
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