Dimensionality Reduction in Hyperspectral Image Analysis Using Independent Component Analysis

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 • Criterion: wavelength [1]

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 Decorrelate Normalize 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, http://spaceresearch.nasa.gov/general_info/light_06-2002_lite.html • 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, 16-20 November 1998. 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. 942-948, 2000 • S. Chiang, et al. “Unsupervised hyperspectral image analysis using independent component analysis”. In Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000, volume 4, pages 3136 – 3138, Honolulu, HI, USA, 24-28 July 2000. IEEE.