visualization of hyperspectral images roberto bonce & mindy schockling imagine reu montclair...
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VISUALIZATION OF HYPERSPECTRAL IMAGES
ROBERTO BONCE & MINDY SCHOCKLINGiMagine REU Montclair State University
Presentation Overview
Hyperspectral Images Wavelet Transform MATLAB code and results Conclusions References
Problem Statement
How can hyperspectral data be manipulated to enable visualization of the important information they contain?
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
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
Wavelets: “small waves”
Decay as distance from the center increases
Have some sense of periodicity
Can perform local analysis unlike Fourier
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
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
Project Cont.
The plant to the right contains both real and artificial leaves
Goal: distinguish between real and artificial leaves
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
Preliminary results
Tried weighting, wavelet transform, different focus bands.
Results were somewhat disappointing
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
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
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
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
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
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
Idea #1 results
Figure 16 pixel vector chosen from brick wall, plant pot, and dark rock. Used local maxima
Idea #1 results
Figure 17 pixel vector chosen from brick wall, plant pot, and dark rock. Used local minima
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
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
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