Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis
In this paper, we propose a method to reduce spectral dimension based on the phase of integrated bispectrum. Because of the excellent and robust information extracted from the bispectrum, the proposed method can achieve high spectral classification accuracy even with low dimensional feature. The c...
Saved in:
| Main Author: | |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
|
| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/4097/1/2-SPECTRAL_DIMENSIONALITY_REDUCTION.pdf http://eprints.utem.edu.my/id/eprint/4097/ http://spiedigitallibrary.org/proceedings/resource/2/psisdg/8180/1/81801H_1?isAuthorized=no |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | In this paper, we propose a method to reduce spectral dimension based on the phase of integrated bispectrum.
Because of the excellent and robust information extracted from the bispectrum, the proposed method can achieve
high spectral classification accuracy even with low dimensional feature. The classification accuracy of bispectrum
with one dimensional feature is 98.8%, whereas those of principle component analysis (PCA) and independent
component analysis (ICA) are 41.2% and 63.9%, respectively. The unsupervised segmentation accuracy of
bispectrum is also 20% and 40% greater than those of PCA and ICA, respectively. |
|---|