1. VISUALIZATION OF HYPERSPECTRAL IMAGES ROBERTO BONCE & MINDY SCHOCKLING iMagine REU Montclair State University

2. Presentation Overview  Hyperspectral Images  Wavelet Transform  MATLAB code and results  Conclusions  References

3. Problem Statement  How can hyperspectral data be manipulated to enable visualization of the important information they contain?

4. What are hyperspectral images?  Most images contain only data in the visible spectrum  Hyperspectral images contain data from many, closely spaced wavelengths  Our camera records data from 400nm to 900nm

5. Hyperspectral cont.  Hyperspectral images can be thought of as being stacked on top of each other, creating an image cube  A pixel vector can be used to distinguish one material from another

6. Pictures

7. Wavelets: “small waves”  Decay as distance from the center increases  Have some sense of periodicity  Can perform local analysis unlike Fourier

8. Wavelet Analysis and Reconstruction  Original signal is sent through high and low pass filters  Approximation: low frequency, general shape  Detail: high frequency, noise  Reconstruction involves filtering and upsampling

9. Noisy Sine

10. The Project  Analyzing hyperspectral signatures for image analysis can be very computationally expensive  One approach to the problem is to select a subset of the images and apply a weighting scheme to generate a useful image

11. Project Cont.  The plant to the right contains both real and artificial leaves  Goal: distinguish between real and artificial leaves

12. Last Year (2007)  Focus bands were chosen  Applied a weighting scheme To give near infrared data more importance because the visual data is too similar  An RGB composite image is created

13. Last Year  Composite image to the right  They used the distance series

14. Preliminary results  Tried weighting, wavelet transform, different focus bands.  Results were somewhat disappointing

15. Procedure  Real leaves have a second peak in near-infrared region  By centering a focus band in this region, real and artificial leaves can be visualized

16. Results Original Image (R:60, G:30, B:20) Band-Shifted Image (R:90, G:30, B:20)

17. Gaussian Weighting  Similar to last approach  Choose 3 focus bands  Use Gaussian curve to do a weighted average of nearby bands  Create RGB composite image  Results are heavily dependent on what focus bands are chosen

18. Gaussian Weighting Figure 9 Weighted average of 3 images near bands 70, 80, and 90. The green leaves are real, the purple leaves are fake

19. Gaussian Weighting Figure 10 weighting using 6 images near bands 20, 30, and 40

20. New Approach  Instead of using 2D images from the cube, use 1D pixel vectors  Idea #1 Choose 3 spectral vectors Do some sort of average Use bands corresponding to the maximum or minimum points to do an RGB composite

21. Idea #1  Take the average of 3 chosen spectra, and take the 3 peaks farthest away from each other  The peaks in the diagram to the right are not very distinct

22. Idea #1  Using a Gaussian curve gives more distinct peaks  The center of the Gaussian curve was the midpoint between the global maxima and global minima of all 3 pixel vectors

23. Idea #1 results Figure 13 real leaf, fake leaf, and pot pixel vectors chosen. Using local maxima

24. Idea #1 results Figure 15 Using the furthest away regional minima, rather than regional minima.

25. Idea #1 results Figure 16 pixel vector chosen from brick wall, plant pot, and dark rock. Used local maxima

26. Idea #1 results Figure 17 pixel vector chosen from brick wall, plant pot, and dark rock. Used local minima

27. Idea #1 results Figure 18 pixel chosen were brick, fake leaf, and rock. Used local minima.

28. Idea #1 results Figure 19 pixel chosen were brick, fake leaf, and rock. Used local maxima.

29. New Approach  Idea #2 Choose pixels of interest Perform wavelet decomposition Identify coefficient positions with maxima Perform decomposition on all pixels Use chosen coefficients to produce a color image

30. Idea #2 Results  Chose 1 pixel within a real leaf and 1 pixel in brick wall for “pixels of interest”  Maxima identified for use as color values R:44 G:20 B:28

31. Idea #2 Results  Top: results using wavelet coefficients  Bottom: results using bands directly

32. Conclusions  Using wavelet coefficients could provide a superior means for visualization in some cases  Computationally expensive  More precise method for selection of pixels/peaks is needed

33. References:  http://www.microimages.com/getst art/pdf/hyprspec.pdf http://www.microimages.com/getst art/pdf/hyprspec.pdf  Images from http://www.wikipedia.org/ http://www.wikipedia.org/  MATLAB help