1 / 33

VISUALIZATION OF HYPERSPECTRAL IMAGES

VISUALIZATION OF HYPERSPECTRAL IMAGES. ROBERTO BONCE & MINDY SCHOCKLING iMagine REU Montclair State University. Presentation Overview. Hyperspectral Images Wavelet Transform MATLAB code and results Conclusions References. Problem Statement.

cicero
Download Presentation

VISUALIZATION OF HYPERSPECTRAL IMAGES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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/getstart/pdf/hyprspec.pdf • Images from http://www.wikipedia.org/ • MATLAB help

More Related