Abstract
A new algorithm for unsupervised hyperspectral data unmixing is investigated, which includes a modified minimum noise fraction (MNF) transformation and independent component analysis (ICA). The modified MNF transformation is used to reduce noise and remove correlation between neighboring bands. Then the ICA is applied to unmix hyperspectral images, and independent endmembers are obtained from unmixed images by using post-processing which includes image segmentation based on statistical histograms and morphological operations. The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pixels. Meanwhile, the results show the high computational efficiency of the modified MNF transformation. The time consumed by the modified method is almost one fifth of the traditional MNF transformation.
| Original language | English |
|---|---|
| Pages (from-to) | 471-476 |
| Number of pages | 6 |
| Journal | Journal of Harbin Institute of Technology (New Series) |
| Volume | 12 |
| Issue number | 5 |
| State | Published - Oct 2005 |
Keywords
- Adaptive subspace decomposition
- Independent component analysis
- Linear mixture model
- Minimum noise fraction
- Spectral mixture analysis
Fingerprint
Dive into the research topics of 'Unsupervised linear spectral mixture analysis with AVIRIS data'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver