This program applies a spectral low pass filter to a cube. A spectral filter
works between bands as opposed to a spacial operation on a single band.
Lowpass filter means it will be subtracting the average from the original
pixel.
This is the total number of bands in the boxcar. It must be odd and
can not exceed twice the number of bands in the cube. In general, the
size of the boxcar does not cause the program to operate significantly
slower.
Valid minimum pixel value that will be used in boxcar computation. If
a pixel value is less than LOW then it will not be used when computing
the average.
Valid maximum pixel value that will be used in boxcar computation. If
a pixel value is greater than HIGH then it will not be used when
computing the average.
This example shows a spectral lowpass filter being applied to the image
with a 5 depth boxcar. This cube has 256 bands so 5 is relatively small.
Although no averaging is done within a single band, the averaging of the
pixels of many bands will cause some averaging and smoothing of the
image if there is much difference between bands. Here is an image which
highlights which pixels a single pass would average. The image shows a
9x9 dot instead of a single pixel highlighted to make it more visible.
In this picture the boxcar would be seven or more bands, with the output
pixel being one of the visible pixels. Depending on how large the boxcar
was many of the values taken in could not be original, at a minimum, one
value will be mirrored since there are only six available bands.
This example will filter the image using 5 bands and no limits on high
or low.
GUI Screenshot
This program's GUI
Example GUI
Screenshot of the GUI wit
h parameters set to perform the
spechighpass filter with a 5 band boxcar on the image
CM_1514302573_1.ir.cub.
Input Image
The image graph before the filter
Input image spectral graph before speclowpass filter
Parameter Name:
FROM
This is the spectral plot of the center point of the image, (32,
32), and shows all bands, 1- 256. No averaging has been applied,
this is clear partialy because of th extreme peaks visible in the
first half. The peaks are related to values which differ
significantly from the bands around them.
Output Image
The image graph after the filter
Output image spectral graph after being filtered
Parameter Name:
TO
This is the image after the filter. Although there are still peaks,
everything has been smoothed. In the before image the peaks were
sharp where as now they are more curved. Differences have been
leveled out to some extent.
Example 2
Example of usage of the spectral lowpass filter.
Description
This example shows a spectral lowpass filter being applied to the image
with a 61 depth boxcar. This cube has 256 bands so 61 takes in over a
fifth. Although no averaging is done within a single band, the averaging
of the pixels of many bands will cause some averaging and smoothing of
the image if there is much difference between bands. In this case a
blurry image begins to show through.
This example will filter the image using 61 bands and no limits on
high or low.
GUI Screenshot
This program's GUI
Example GUI
Screenshot of the GUI with parameters set to perform the
spechighpass filter with a 61 band boxcar on the image
CM_1514302573_1.ir.cub.
Input Image
The image graph before the filter
Input image spectral graph before speclowpass filter
Parameter Name:
FROM
This is the spectral plot of the center point of the image, (32,
32), and shows all bands, 1- 256. No averaging has been applied,
this is clear partialy because of th extreme peaks visible in the
first half. The peaks are related to values which differ
significantly from the bands around them.
Output Image
The image graph after the filter
Output image spectral graph after being filtered
Parameter Name:
TO
This is the image after the filter. As is very clear, the averaging
has turned sharp peaks into a smooth slope and small bumps into
nothing. All bands in the image are now muchmore similar to each
ohter than they were before. Also note that the scale has changed
significantly.
