EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA
Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/459/2015/isprsarchives-XL-3-W3-459-2015.pdf |
| Summary: | Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral
data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications.
Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal
band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms.
On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum
and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance
criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy.
Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm
to this hierarchy of merged bands. |
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| ISSN: | 1682-1750 2194-9034 |