CEE 615: Digital Image Processing
Lab 12: Continuum removal
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Hyperspectral Processing: continuum removal
A major advantage of hyperspectral data is the possibility of identifying specific materials. This is
generally only possible to the extent that the material in question has unique absorption features or, more
likely, a set of absorption features that together allow a high level of specificity. A problem with this
type of identification is that the absorption features tend to be relatively subtle, and the variation in the
absorption features tends to be even more subtle. One obvious consequence is that one cannot equate
variance in an image with the quality of the information in that image. This makes it more difficult to
sort out the useful information from the background noise.
In this lab the task is to isolate a pair of subtle water absorption features in order to look for the presence
of water on or very near the surface in a very arid environment. The processing will include steps to
remove noise as well as to characterize variations in the depth of the absorption bands.
The image, collected by the Hyperion instrument flown on the EO-1 satellite, is of a portion of desert in
Qatar. The image was collected on 3 March 2010 at 9:45 AM local time. The imaging system collects
220 bands with a 10-nanometer spacing from 0.4-2.6 microns. It is a proof-of-concept instrument and
samples at a 30 m pixel spacing with a FOV of 7.5 km.
The processing sequence is as follows:
1. Load the original image data: Qatar2010.img
2. Apply an FFT filter to remove banding noise
3. Perform an MNF transform on a limited spectral range of the data containing the target water
absorption feature(s)..
4. Analyze the results by displaying the spectrum (z-profile) for both the unfiltered and filtered
image
5. Apply the continuum removal algorithm on the resulting data to optimize for water absorption.
Now, step by step:
1. Load the original image data: Qatar2010.img. The image should automatically display as a CIR
image. Red will generally correspond to vegetation.
2. Apply an FFT filter to remove banding noise
The banding noise in this imagery is rather severe and will make it difficult to observe subtle
band-to-band variations. Since the banding noise is very identifiable in the frequency domain, an
appropriate FFT filter can be very effective at eliminating a majority of the banding noise
without an appreciable effect on the image data. With this in mind:
a. Apply the Fast Fourier Transform (Filter > FFT Filtering > Forward FFT) to the
Qatar2010.img
b. Apply the inverse FFT using the filter, banding_rectangular-filter.img, naming the result
Qatar2010_invFFT.
c. Display the filtered image and inspect.
CEE 615: Digital Image Processing
Lab 12: Continuum removal
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wavelength (nm)
Figure 1: The water absorption bands at 941, 1127, 1390 and 1880 nm are not very reliable in
Hyperion imagery. The absorption bands do not vary significantly in depth relative to the values at
the wings of the absorption bands. Bands at 721 and 823 nm appear to be more useful. Even here the
images in this spectral range are quite noisy and the sought-after features are rather subtle.
3. In order to optimize the information content of the image set, perform a Minimum-NoiseFraction (MNF) filtering on the target spectral range (671-884 nm; bands 32-53) containing the
target absorption features.
a. Select: Transform > MNF Rotation > Forward MNF > Estimate Noise Statistics from
Data
b. Select the image: Qatar2010_invFFT. OK.
c. Select the spectral band subset 32-53. (Highlighted bands are the bands that are selected)
d. Create a file for the noise statistics: Qatar2010_invFFT_MNF32-53-noise.sta
e. Create a file for the MNF statistics: Qatar2010_ invFFT_MNF32-53.sta
f. Name the output file: Qatar2010_ invFFT_MNF32-53.img
g. Select OK
h. Examine the MNF image data.
i. The first MNF image is dominated by viewing angle effects. Since there is a strong
relationship between the water vapor content and the atmospheric path length, this is
probably indicative of water vapor content. MNF bands 2, 3, 4 and 5 all appear to have
significant information about the land surface (non-noise) and should certainly be kept.
MNF bands 6 and 7 contain little information about the land surface, but do appear to
contain non-random features that are probably related to atmospheric variability. Bands
8 and 9 are primarily noise but do have minor features related to the dunes and some of
the vegetation patches.
j. Select the useful MNF images. Certainly keep bands 2-5. Keep band 1 if you want to
preserve the effects of the atmospheric path. I would opt to keep bands 6 and 7 as well
since they are not illustrating random noise. Bands 8 and 9 are more questionable, but
beyond 9, none of the MNF images have discernable features. At most, retaining 9-bands
(1-9) is sufficient to retain anything but random noise.
CEE 615: Digital Image Processing
Lab 12: Continuum removal
p. 3
k. Apply inverse transform.
i. Select Transform > Inverse MNF Transform.
l. Select the image: Qatar2010_ invFFT_invMNF33-53_bi-bf.img (where bi is the first
MNF band used and bf is the final MNF band used.
ii. Select OK.
iii. Select the MNF stats file: Qatar2010_ invFFT_MNF33-53.sta
iv. Name the output file Qatar2010_ invFFT_invMNF33-53_b2-x.img (where x
represents the highest MNF band being retained).
v. Select OK
4. Analyze the results by displaying the spectrum (z-profile) for both the unfiltered and filtered
image. Link the images and move the cursor around the image. If this worked the spectra
should be relatively stable, but the depth of the water absorption band will change.
5. Apply the continuum removal algorithm on the resulting image to optimize for water absorption.
Finally, the data are ready for continuum removal.
a. Select Spectral > Mapping Methods > Continuum Removal
b. Select Qatar2010_ invFFT_invMNF33-53_b2-x.img as the input file. OK.
c. Select Memory and OK.
Note that in the final image set, the bands forming
the tie points for the normalization are all the
same gray value and appear black. Other images
in the data sets display a variety of features.
Pay particular attention to the images
corresponding to wavelengths 721 nm and
823 nm, corresponding to the center of two of the
water absorption bands. These are the most likely
to show the effects of atmospheric water
absorption.
Figure 2: Spectra (z-profiles) of the original data
and the continuum removed data. The water
absorption bands at 721 nm (red) and 823 nm
(green) are marked.