1. Dimensionality Reduction in Hyperspectral Image Analysis Using Independent Component Analysis
Hongtao Du
Feb 18, 2003

2. Status Summary What is hyperspectral image?
How to analyze hyperspectral image?
Why reduce dimensionality in hyperspectral image analysis?
What is independent component analysis?
How to use ICA to reduce dimensionality in HSI?
Any parallel solution?
Implement on FPGA?

3. Electromagnetic Spectrum
[1]
Criterion: wavelength

4. Number of Bands in Spectral Images

5. Multispectral Image Simplest one: RGB images
Not necessarily contiguous
Multispectral sensor collect data
Simultaneously
Sequentially

6. Hyperspectral Image In narrow and contiguous wavelength bands.
Most Hyperspectral
100~300 bands
Interval < 15 nm
Hyperspectral sensor collect data
Simultaneously

7. Spectral analysis
Soil
Rock
Water
Vegetation

8. Analysis Spaces
Image Space
Spectral Space
Feature Space

9. Analysis Spaces (Cont’)
Image space
Pixels are displayed in grey scale.
Spatial analysis
Spectral space
Pixels are functions of wavelength.
Spectral analysis
Feature space
Pixels are points in N-dimensional space.
Relationships among Pixels

10. Challenges and Approaches

11. FastICA Algorithm Initialize Weight Vector Update Normalize One Unit
Update
Normalize
Decorrelate
Next
One Unit
Process
Loop until
Converge
Multiple
Units
Decorrelation
Loop until
Converge

12. Using ICA to Reduce Dimensionality

13. Parallel ICA
Internal
Decorrelation
External
Decorrelation

14. Parallel ICA Diagram

15. Performance Comparison

16. Advantage and Disadvantage of FPGA
Advantages
Speedup
Parallel computation
Disadvantages
Complexity
Size of data set

17. Synthesis Processes

18. Conclusions HSI and HSI Analysis Reduce dimensionality ICA
Using ICA to reduce dimensionality
Parallel ICA
Implement on FPGA

19. References
The office of Biological & Physical Research, “Introduction of Space Research”, NASA,
M. Velez-Reyes et al. “Comparison of matrix factorization algorithms for band selection in hyperspectral imagery”. In SPIE 14th Annual International Symposium on Aerospace/Defense Sensing, Simulation and Controls, volume 4049(2000), pages 288–297, 2000.
P. Hsu and Y. Tseng. “Primary study of fourier spectrum feature extraction for hyperspectral image”. In The 19th Asian Conference on Remote Sensing, Manila, November Asian Association of Remote Sensing (AARS).
E. Winter and M. Winter. “Autonomous hyperspectral end-member determination methods”. In EUROPTO Conference on Sensors, Systems, and Next-Generation Satellites V, volume 3870, pages 150–158, Florence, Italy, September 1999.
S. Subramanian, et al. “Methodology for hyperspectral image classification using novel neural network”. In A.Evan Iverson and Sylvia S. Shen, editors, SPIE: Algorithms for Multispectral and Hyperspectral Imagery III, volume 3071, pages 128–137, Orlando, FL, USA, April 1997.
L. Parra and S. Sajda. “Unmixing hyperspectral data”, Advances in Neural Information Processing Systems 12, MIT Press, pp , 2000
S. Chiang, et al. “Unsupervised hyperspectral image analysis using independent component analysis”. In Geoscience and Remote Sensing Symposium, Proceedings. IGARSS 2000, volume 4, pages 3136 – 3138, Honolulu, HI, USA, July IEEE.