Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3057117 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Gray-scale, Correlation, Feature extraction, Complexity theory, Morphology, Computer science, Toy manufacturing industry, Band selection, dilation, feature selection, hyperspectral images, mathematical morphology (MM) | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Challa | 1 | 3 | 4.10 |
| Geetika Barman | 2 | 0 | 0.34 |
| Sravan Danda | 3 | 3 | 4.10 |
| B. S. Daya Sagar | 4 | 0 | 0.34 |