Aerospace and Electronic Systems Magazine June 2017 - 29
Cohenour, Rovito, and van Graas
The pixel errors are given in Table 5. The worst case intensity
error is 18 out of 256. The histogram is shown in Figure 8.
Figure 9 shows the projected raw image on the left and the
projected reconstructed raw image after compression and decompression on the right for frame 236 of the 20091021 data set. The
compression ratio is 6.73 to1. There is no discernable difference
between the two images.
The loss of resolution for the 20091021 data set has been measured. The projected images have a mean resolution index of -4.25.
The raw images have a mean resolution index of -2.8. When compressed by a 6.73 to 1 ratio the raw images have a mean resolution
index of -2.9. The loss of resolution in the projected images is from
1.24 lp/m to 1.05 lp/m, while the loss due to compression is from
1.24 lp/m to 1.23 lp/m. The total loss of resolution is 0.19 lp/m
while the resolution lost due to compression is 0.01 lp/m. Compression can only account for 5% of the lost resolution. 95% of the
lost resolution is from resampling.
Figure 8.
Histogram for 6.73 to 1 compression.
MAXIMIZING IMAGE RESOLUTION
In Figure 5, the loss of resolution is clear. The compression loss is
illustrated in Figure 9. The compression loss does not explain the
Table 5.
Compression Error for 240 Raw Images with a
Compression Ratio of 6.73
Intensity
Error
Percentage of
Total Points
0
2,348,610,000
62.766 380 496
1
834,436,000
22.300 223 313
2
412,562,000
11.025 680 496
3
146,220,000
3.907 715 694
4
41,919,500
1.120 294 679
5
10,515,600
0.281 028 417
6
2,584,280
0.069 064 639
7
690,839
0.018 462 607
8
198,830
0.005 313 713
9
58,437
0.001 561 723
10
17,323
0.000 462 956
11
4,943
0.000 132 101
12
1,451
0.000 038 778
13
438
0.000 011 706
14
137
0.000 003 661
15
34
0.000 000 909
16
8
0.000 000 214
17
3
0.000 000 080
18
2
0.000 000 053
3,741,828,000
100.000 000 000
Total
JUNE 2017
Number of Pixels
Figure 9.
Comparison of projected raw image, and projected compressed image,
compression ratio is 6.73 to 1, frame 236.
resolution loss in Figure 5. To maximize resolution, especially at
high zoom levels, the pcolor projection technique is much better.
The technique proposed and prototyped here is to move the
projection from the beginning of the process to the end of the process, see Figure 1. This requires that the projection be performed
by the SET. This is a significant change, and a number of questions
must be addressed. First, what data is required for the SET to project the image on demand? Second, how will the data transmission
and storage requirements change? In this case there is a data link
of fixed bandwidth so the size of the compressed images cannot
increase significantly. Finally, because the projection must be performed on demand what can be done to ensure that the analyst does
not experience latency or delay?
For the traditional SET model, the NITF image is geo-rectified.
The only information required is the Ground Sample Distance, and
a single reference location in latitude and longitude, or other coordinate system.
For the revised SET model, in addition to the raw images,
information for the projection is required. For the projection, the
camera pose, a camera model, and an elevation data base are necessary. The camera pose includes the camera position and attitude
for each frame of data. The camera calibration includes the intrinsic and extrinsic parameters for the camera. In this case there are
IEEE A&E SYSTEMS MAGAZINE
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