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

2. Presentation Overview  Hyperspectral Images  Wavelet Transform  Denoising  MATLAB code and results  Future Work  References

3. What are hyperspectral images?  Most images contain only data in the color spectrum  Hyperspectral images contain data from several, continuous wavelengths  Our camera records data from 400nm to 900nm

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

5. Pictures

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

7. 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

8. Noisy Sine

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

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

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

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

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

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

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

16. Future Work  Further explore the use of wavelets for denoising data  Continue to investigate various weighting schemes  Attempt to classify or distinguish between other materials besides leaves

17. 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