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VISUALIZATION OF HYPERSPECTRAL IMAGES

VISUALIZATION OF HYPERSPECTRAL IMAGES. ROBERTO BONCE & MINDY SCHOCKLING iMagine REU Montclair State University. Presentation Overview. Hyperspectral Images Wavelet Transform Denoising MATLAB code and results Future Work References. What are hyperspectral images?.

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VISUALIZATION OF HYPERSPECTRAL IMAGES

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

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