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Precise error correction method for NOAA An Ngoc Van Yoshimitsu Aoki

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Precise error correction method for NOAA An Ngoc Van Yoshimitsu Aoki
Precise error correction method for NOAA AVHRR image using the same orbital images
127
Precise error correction method for NOAA
AVHRR image using the same orbital images
An Ngoc Van1 and Yoshimitsu Aoki2 , Non-members
ABSTRACT
NOAA images provide very useful information
about the Earth and have been widely using. This
paper proposes a method that precisely corrects the
errors in NOAA images. NOAA images with the same
orbit are received at various stations and they overlap
each other. The errors in the original image, including missing lines and error lines, are corrected by using reference images, which are received at other stations and overlap the original one. An error information database is used to select the highest quality reference images. After specifying the overlapped area,
missing lines are detected by checking time codes, error lines are recognized based on PN codes. Missing
lines and error lines are corrected by using the values
of the corresponding lines from reference images. As
a result, missing data can be fully restored and errors
are precisely corrected.
This method was used to correct the errors in
the NOAA images receiving at Tokyo, Bangkok and
Ulaanbaatar. The correction results proved its high
precision. The correction time was less than 37 seconds per image.
Keywords: NOAA AVHRR, error correction, overlapped area, reference image
1. INTRODUCTION
In recent years, AVHRR (Advanced Very High
Resolution Radiometer) on the NOAA (National
Oceanic and Atmospheric Administration) series of
satellites has been an ideal observatory for daily
global observation of the Earth. NOAA images provide very useful information about ecosystems, climate, weather and water from all over the world.
NOAA images are also widely used in land cover monitoring at global and continental scales [1].
Because of many reasons which occur in scanning,
sampling, transmission or recording processes, errors
appear in NOAA images. In order to use NOAA images effectively, error correction method is necessary.
Manuscript received on March 1, 2007 ; revised on July 3,
2007.
1 The author is with the Functional Control Systems
Course, Shibaura Institute of Technology, Japan, Emails:
[email protected]
2 The author is with the Dep. of Information Science and
Engineering, Shibaura Institute of Technology, Japan, Email:
[email protected]
Some methods to correct the errors in NOAA images were proposed. In the conventional correction
methods [2], errors are corrected by using only the
relations between the lines and pixels in the original
image; therefore, a lot of errors could not be recognized and the accuracy of the results was not very
high. Recently, an effective method which corrects
errors by referring to the reference images that overlap the original one were introduced in [3,4]. According to this method, the overlapped areas between the
original image and the reference one are specified by
matching the time codes of the lines in both images,
and errors are corrected by using data in this overlapped area. Compared to other correction methods
that also use overlapped images [5,6], whereas it is
quite difficult and complicated to specify the overlapped areas in those methods due to the processes of
extracting, comparing or matching the features and
templates, it is easy but precise to locate the overlapped areas with the method of [3,4]. However, when
an original image has many overlapped images, this
method could not select the most suitable reference
images to optimize the correction. For this reason,
bad quality reference images might be selected. In
this case, the overlapped area in the reference images
will contain many errors, and not all of the errors in
the original images will be removed.
This paper improves the method of [3,4] by assessing the quality of the reference images and selecting the best ones to use. First, an error information
database is created by collecting the error information in NOAA images. Then, based on the database,
a method is proposed in order to find the highest quality reference images among available ones. Hence, the
best quality reference images can be selected, and the
number of errors in the overlapped area of reference
images will be minimized. As a result, almost all of
the errors in the original image will be corrected.
2. HRPT FORMAT
NOAA images contain lines, and each line consists
of pixels. They are received in HRPT (High Resolution Picture Transmission) format. This format
includes time code, PN code and AVHRR data.
2. 1 Time code
AVHRR sensor scans 6 lines per second. Time
code is the time when the sensor starts to scan a line.
It is recorded for each line, and it includes month,
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ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS VOL.5, NO.2 August 2007
day, hour, minute, second and millisecond. The unit
of time code is millisecond.
2. 2 PN code
PN code is a pseudo-random sequence provided by
a feedback shift register. There are three PN codes
in HRPT format. The first PN code is 60 bits long
and it can be used to specify the beginning data of
a line. The second PN code includes 1270 bits and
the third one includes 1000 bits. The total length of
all PN codes is 2330 bits. These bits have pre-fixed
values for each line of a NOAA image. When a line
contains error bits, its PN codes will differ from the
fixed value. As a result, PN codes can be used to
detect the error lines. The error rate of each line is
the ratio of PN code to 2330. A line will contain error
when its error rate is greater than 0.
2. 3 AVHRR data
The AVHRR data that sensor obtains from the
Earth is stored in the lines of image. Each line includes five data channels. Channels 1 and 2 are used
to monitor land surface processes, whereas channels
3, 4 and 5 are used for sea surface temperature determination and cloud mapping [1]. Every channel in a
line contains 2048 pixels. A pixel is coded in 10 bits.
3.
Errors in NOAA image can be due to the errors
in the scanning or sampling equipment, in the transmission or recording of data, or in the reproduction
of the media containing the data [1]. In this study,
all errors in NOAA images are divided into missing
lines and error lines.
data. At the time when the data of the top or bottom
part in NOAA images are being received, the distance
from satellite to receiving station is long; hence, errors will occur.
4. CURRENT CORRECTION METHODS
4. 1 Conventional method
As was shown in [2], this method corrects errors
by using only the relation of the lines and pixels in
the original image.
First, missing lines are detected by finding the
missing time codes in the image. A blank line is then
inserted into image to replace the missing line. For
this reason, all missing data is lost. Then, error pixels
are recognized by comparing their values with their
neighbors’. According to the statistical results [2],
the difference in the values between an error pixel
and its neighbors is about 512, 256, 128, 64 or 32.
Once being detected, an error pixel is corrected by
adding or subtracting this different value. Therefore,
an error pixel is probably assigned a wrong value.
Furthermore, conventional method also could not detect all the error pixels in the original images. Table 1 shows the number of error pixels detected by
this method of the image AH16060902052744 from
Tokyo, and the actual number of error pixels detected by comparing this image with the overlapped
image AH16060902052551 from Bangkok. The result of this table is calculated on 107 error lines in
the overlapped area of the image from Tokyo. The
corresponding lines in the image from Bangkok are
correct lines (they are neither missing lines nor error
lines). Clearly, the number of error pixels detected by
this method is much smaller than the actual number
(12978/36017).
3. 1 Missing lines
Normally, the time code of each line in NOAA
images is recorded. However, when the sensor cannot get the data of a line, its time code will not be
recorded, and it is considered as a missing line. Because the sensor scans 6 lines per second, a missing
line can be detected by checking the time codes of
other lines. The missing time code can also be inferred.
3. 2 Error lines
A line in NOAA image might contain error pixels.
When a line contains one or more error pixels, it is an
error line. The error line’s PN codes will differ from
its fixed PN codes. Therefore, an error line can be
detected by checking its PN codes.
3. 3 Error areas in NOAA images
The statistical results of [2] have shown that errors
appear only at the top and bottom parts of NOAA
images. The reason is the receiving systems of NOAA
Table 1: Error pixels in 107 lines
Errors
Detected
Actual
]512
2920
2502
]256
3825
3995
]128
2708
2916
]64
2052
2599
]32
1406
2283
All errors
12978
36017
4. 2 Method using reference images
Another correction method using reference images
was introduced in [3,4]. NOAA images with the same
orbit are received at various stations and they overlap
each other. The data in the overlapped area of the
reference images can be used to correct the errors in
the corresponding area of the original image.
First, the reference image which overlaps the original one is selected. Next, the overlapped area in
both images is specified by matching the time codes.
Then, missing lines are detected by checking the time
codes of the original image. They are corrected by assigning the values of the corresponding lines from the
reference image. If the corresponding line is also a
Precise error correction method for NOAA AVHRR image using the same orbital images
missing line, a blank line is inserted to the original
image. Final, the error lines, whose error rates are
greater than zero, are detected by checking the PN
codes. They are corrected by assigning the values
of the corresponding lines in the reference image. If
the corresponding lines are also error lines, the error pixels in the error lines of the original image are
corrected by using the conventional method.
With this method, the missing lines are restored
only when their corresponding lines are correct lines.
Similarly, the error lines are precisely corrected only
when the corresponding lines are not missing lines
and their error rates are zero.
In the case that the corresponding lines are missing
lines or error lines, errors are corrected by using the
method of [2]. Thus, the missing data in the missing
lines will be lost, the error pixels might be assigned
wrong values and many error pixels still cannot be
recognized. Furthermore, when an original image has
many reference images, this method could not select
the highest quality reference images. The bad quality
image with a lot of errors in the overlapped area might
be used; hence, not all errors in the overlapped area
of the original image could be corrected.
129
the first line.
For example, the NOAA image
AH14012599191054 was scanned at the time 19:10:54
on January, 25th, 1999. Every NOAA image has less
than 6500 lines. Because the sensor scans 6 lines per
second, the total time to scan a whole NOAA image
is less than 1100 (6500/6) seconds. Therefore, two
images with the same orbital data may overlap each
other if the absolute value of the difference in the time
extracting from their names is less than 1100 seconds.
The time difference between the original and reference image is an algebraic value. It will be a positive
number when the original image is received at later
time than the reference one is and vice versa.
Figure 2 shows an example of which the image
AH16062602054308 from Tokyo overlapped and image AH16062602053903 from Bangkok. The image
from Tokyo is received at 05:43:08 and the one from
Bangkok is at 05:39:03 on June, 17th, 2002. The time
difference between them is +245 seconds.
5. PROPOSED METHOD
In order to improve the method using reference
images, a new method is proposed. Figure 1 is the
steps of new method.
Fig.2: Overlapped images
Fig.1: Steps of proposed method
NOAA images have been receiving at many stations. Therefore, an original image may have many
reference images. The new method will find all available reference images from receiving stations. Their
quality will be assessed based on the error information
database. The reference image with highest quality
will be selected first and the overlapped area will be
specified. Data in this area will be used to correct errors. If there are still errors in the overlapped area of
the original image after correction, reference images
with lower quality will be used.
5. 1 Finding available reference images
A NOAA image is saved as a file whose name
includes the time when the sensor started to scan
5. 2 Error information database
After available reference images are found, they
need to be analyzed to detect errors and specify the
overlapped areas before use. If the analyzing is applied to all reference images, it will take a long time
compared to the total correction time. Moreover,
in many cases, only one or two reference images are
enough to correct all errors in the original image and
it is not necessary to analyze the rest ones. For this
reason, statistics are carried out with NOAA images.
The error information in NOAA images is collected,
and it will be used to assess the quality of the reference image. Furthermore, after collecting error information, the results will be used not only for the images belonging to the statistical process but also for
other images. As a result, the information to assess
reference images will be available without spending a
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ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS VOL.5, NO.2 August 2007
long time to analyze all of them.
Figure 3 is a NOAA image with errors at the top
and bottom parts. As was shown in [2], errors in
NOAA images appear at the top and the bottom
parts. In this image, the Error Top and Error Bottom
area are the error area at the top and bottom part,
respectively. In the direction from top to bottom, the
final line of Error Top area and the first line of Error
Bottom area are missing or error lines. The rest area
is correct (do not contains any missing or error line).
When the NOAA image in figure 3 is used as reference image, the overlapped area should locate within
the correct area to minimize the number of errors.
For this reason, the total lines, the number of lines in
the Error Top and Error Bottom area are collected
when doing statistics.
The information of each image using in statistical process are stored in the error information
database. Therefore, if a reference image belongs to
the database, its information will be available immediately. In addition, to improve the correction result
for the images which do not belongs to the database,
the information will be calculated from the database
with a cumulative parameter. For example, when a
NOAA image from Bangkok which does not belong
to the database is used as a reference image, and the
cumulative parameter is 90%, the information getting from the database will be ET=669, EB = 836,
T L = 4723, which means that the 90% images from
Bangkok in the database have ET <= 669 lines,
EB <= 836 lines and T L <= 4723 lines. The cumulative parameter is necessary because if only the
maximum value (the cumulative parameter is 100%)
in the database is used, the overlapped area will be
too small (contains not all errors in the original image); similarly, if only the minimum value (the cumulative parameter is approximately 0%) is used, the
overlapped are will be too large (contains a lot of errors in the reference image); and if the average value
is used, the overlapped in the original image may still
contain errors with high probability.
Table 3: Statistical result with images from Tokyo
Fig.3: Statistical information
The statistics are carried out with the NOAA images receiving at Tokyo (Japan), Bangkok (Thailand)
and Ulaanbaatar (Mongolia), from June to October
in 2002. In each month, about 90% number of images is used for statistics and the rest 10% is for testing. Table 2 is the number of NOAA images using
for statistics (upper row for each station) and testing
(lower row for each station).
Table 2: Error pixels in 107 lines
Station
Tokyo
Bangkok
Ulaanbaatar
Jun
106
11
82
9
73
8
Jul
103
11
104
11
83
9
Aug
116
12
98
10
87
9
Sep
108
12
107
11
93
10
Oct
101
11
89
9
92
9
Total
534
57
480
50
428
45
The statistical results for NOAA images from
Tokyo, Bangkok and Ulaanbaatar are shown in Table 3, 4 and 5. In these tables and the figures below,
ET and EB is number of the lines in the Error Top
and Error Bottom area of the image, respectively; T L
is total lines in the image. The information of the
Ave. columns is the average values calculating from
the information in the tables 3, 4, 5 and the number
of images in the table 2.
Information
Max
ET
Min
Ave.
Max
EB
Min
Ave.
Max
TL
Min
Ave.
Jun
621
0
163
701
32
242
5894
63
4567
Jul
664
0
166
775
36
295
5916
157
5117
Aug
1918
0
211
1999
29
319
5875
57
4975
Sep
2998
0
158
3001
37
289
6001
126
5021
Oct
342
0
107
892
26
233
6156
83
5003
Ave.
1339
0
162
1498
31
276
5965
96
4936
Table 4: Statistical result with images from Bangkok
Information
Max
ET
Min
Ave.
Max
EB
Min
Ave.
Max
TL
Min
Ave.
Jun
2011
30
265
2011
2
250
5699
706
4939
Jul
645
21
228
697
3
300
5636
3483
4946
Aug
746
32
254
834
35
272
5702
2164
4974
Sep
532
44
279
736
17
437
5671
2013
4712
Oct
2374
0
424
2707
20
461
5684
2245
4767
Ave.
1194
26
287
1330
15
346
5676
2182
4865
5. 3 Assessing and selecting reference images
An original image may have some reference images,
but not all of them are always used to correct errors.
Therefore, the available reference images should be
assessed so that the most suitable one will be used
first, and then, if it is necessary, the less suitable
ones will be taken into account. Since the data in
Precise error correction method for NOAA AVHRR image using the same orbital images
131
Table 5: Statistical result with images from Ulaanbaatar
Information
Max
ET
Min
Ave.
Max
EB
Min
Ave.
Max
TL
Min
Ave.
Jun
2322
0
609
2146
1
847
5006
735
4186
Jul
2266
0
604
2154
2
696
5028
638
4297
Aug
2351
0
549
2397
2
673
5087
504
4375
Sep
2259
0
491
2204
2
872
4980
1870
4371
Oct
1843
172
1101
1940
1
1106
4968
3319
4275
Ave.
2200
36
675
2166
2
843
5012
1471
4305
the overlapped area of reference image will be used
to correct errors, available reference images will be
assessed based on the quality of this area. The criteria to assess are: the better quality a reference image
is, the less error appears and the more lines it has in
the overlapped area. The statistical error information
from the database is used to assess the quality.
Suppose that the original image O have n reference
images Ri , i = 1..n. The algebraic time difference
between O and Ri is di , and the number of correct
lines in the overlapped area between O and Ri is li .
Figure 4 is an original image and its ith reference
image. In this case, the original image is received
later than the reference one; thus, di > 0 and the
overlapped area in the reference image is used to correct the errors in the top part of the original one.
In order to correct as many errors in the top part of
the original image as possible, the error areas in the
reference image should not be located in the overlapped area. Therefore, di should be in the range of
dimin and dimax , which are calculated as follow:

 dimin ≤ di ≤ dimax
dimin = ETi
(1)

dimin = T Li − (ETo + EBi )
The number of correct lines in the overlapped area
is:
li = T Li − (di + ETo + EBi )
(2)
In the case of figure 5, the reference image is received later than the original one; therefore, di < 0,
and the overlapped area is used to correct the errors
in the bottom part of the original image. di should be
in the range of dimin and dimax , which are calculated
as follow:

d


 imin
dimin
dimin



dimin
≤ |di | ≤ dimax
= T Lo − (EBo + ETi )
= 0 if T Lo ≤ (T Li − EBi )
= T Lo − (T Li + EBi ) othwise
(3)
The number of correct lines in the overlapped area
is:
li = T Lo − (di + EBo + ETi )
(4)
Fig.4: Original image is later than reference image
Fig.5: Original image is earlier than reference image
Those images Ri whose di > 0 and di satisfies (1)
will be sorted by li calculating by (2). They can be
used as reference images in the descending order of li
to correct the errors in the top part of original image
O. Similarly, those images Ri whose di < 0 and di
satisfies (3) will be also sorted by li calculating by (4).
They can be used as reference images in the descending order of li to correct the errors in the bottom part
of the original image O.
5. 4 Correcting errors
After selecting reference images, the data in the
overlapped area of the reference image will be used
to correct the errors in the original image. The correction steps are shown in figure 6.
First, both original and reference images are analyzed. The time codes will be checked to detect the
missing lines. Because the errors appear at the top
and bottom parts of the image [2], the time codes of
the lines in the middle of image are accurate. Moreover, sensor scans 6 lines per second; consequently,
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ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS VOL.5, NO.2 August 2007
Fig.6: Correction steps
based on the time code of a correct line in the middle
of the image, the accurate time codes for the other
lines are inferred and saved into a time code table.
If a value in the time code table cannot be found in
the image, a missing line is detected, and a blank line
will be inserted to the appropriate position.
Next, to find out the overlapped area, the time
codes of the lines in the original and reference images
are matched. Two lines in two images will contain
the same data if they have the same time code. Consequently, each line in the overlapped area of the original image has a corresponding line in the reference
images and they have same time code.
Once the overlapped area is specified, the errors
in this area of the original image will be corrected.
In order to correct the errors in the top part of the
original image, the reference image with di > 0 are
used; similarly, the reference image with di < 0 are
used to correct the errors in the bottom part of the
original image. When a missing line or an error line
in the original image is detected, if the corresponding line in the reference image is correct line (not a
missing line or error line), its value will be assigned
to the missing line or the error line. As a result, the
missing data can be restored and the error lines will
be corrected. In the case that the corresponding line
is also a missing or error line, the second reference image with smaller li will be analyzed and used. This
process will continue until all errors in the original
image are corrected.
6. TESTING AND EVALUATION
The proposed method is applied to correct the errors in the images receiving at Tokyo, Bangkok and
Ulaanbaatar. Four tests are implemented with sample images. In the first test, the sample images are
selected from the images belonging to the database.
In the second test, the sample images is selected from
the images not belonging to the database but were
received from June to October in 2002. In the third
test, the sample images are selected from the image
not receiving in 2002. Table 6, 7 and 8 are the results
of these tests. In the final test, all types of sample
images (belong to the database or not, were received
from June to October in 2002 or not) are used and
the value of cumulative parameter is changed. Table
9 is the result of this test.
In each test, 20 sample images are used and the
numbers of errors (including missing lines and error
lines) in the original image, before and after correction, are recorded. The cumulative parameter is 95%.
The tests are implemented on the Sun Ultra 45 Workstation with 1.6 GHz Sun UltraSPARC IIIi processor
and 1GB RAM.
The results show that the number of errors remaining after correction is quite small. All most all of the
errors are corrected. In some cases, there is still one
error line in the corrected image. The reason is that,
in these cases, there is only one reference image is
available to correct the errors in the top or bottom
part of the original image;therefore, if the error appears in the overlapped area of the reference image,
the program cannot find any other reference image to
replace, and it will try to correct as many error lines
as possible. Another reason is that the error occurs
at the acquisition process; as a result, all available
references images will contain the same error line as
the original image.
Table 6: Correction result of the first test
Image’s
Index
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total
Before correction
ET
EB
107
124
32
65
243
313
1005
1276
217
312
129
346
174
245
662
664
12
125
121
254
0
342
233
332
674
703
0
121
11
86
275
323
145
188
1243
1643
0
42
68
156
5351
7660
Errors remaining
After correction
ET
EB
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
3
5
0.05%
0.06%
Time
(sec)
27
28
28
30
27
27
26
27
27
28
26
28
29
27
26
27
27
29
26
27
547
Ave.
27.35
Precise error correction method for NOAA AVHRR image using the same orbital images
Table 7: Correction result of the second test
Table 9: Cumulative parameter evaluation
Cumulative
Parameter (%)
70
80
90
91
92
93
94
95
96
97
98
99
100
Table 8: Correction result of the third test
The percent of errors remaining after correction
is bigger when the sample images are not in the
database. The reason is: when sample images belong to database, the information to assess reference
images is real information, which is calculated by analyzing them; when the sample images do not belong
to database, the information to assess reference images is statistical information, which may differ from
real information. Thus, the reference images will be
assessed more accurately when they belong to the
database, and the number errors remaining will be
smaller.
The results of table 7 and 8 are not so different. It
133
Error remaining (%)
ET
EB
0.12
0.11
0.11
0.09
0.09
0.08
0.09
0.08
0.09
0.08
0.09
0.07
0.08
0.07
0.08
0.07
0.08
0.07
0.08
0.08
0.08
0.08
0.09
0.09
0.10
0.09
proves that this correction method can be effectively
applied to the images received at other months or
years.
In the first test, the information about the original and reference images is read directly from the
database;therefore, the correction time is smaller
than the rest testes. In general, the correction time
is less than 37 seconds. Compared to the size of a
NOAA image, which is about 100MB per image, this
is an acceptable correction time.
Table 9 shows that when the cumulative parameter
is around 95%, the percent of errors remaining is the
smallest. As mentioned before, when the cumulative
parameter is 100%, the overlapped area will be too
small, it will not contain all errors in the original image; when the cumulative parameter is smaller (such
as 70, 60 or 50) the overlapped area will be larger, it
will contain more errors in the reference image
Figure 7, 8 and 9 are examples of the first, second
and third test.
In figure 7, the original image from Ulaanbaatar
has two reference images from Bangkok and Tokyo;
both of them can be used to correct the errors in the
bottom part of the original one. Because these images
belong to the database, their error information has
been exactly known. Whereas the overlapped area of
the image from Bangkok is small and its Error Top
area overlaps the Error Bottom area of the original
image, the overlapped area of the image from Tokyo is
large and its Error Top area does not overlap the Error Bottom area of the original image. In the previous
methods, the reference image from Bangkok might be
selected and not all the errors in the original image
are corrected. With proposed method, in this case,
the image from Tokyo is selected as the most suitable reference image. Therefore, all the errors in the
bottom part of the original image are corrected.
In figure 8, the original image from Bangkok has
also two reference images from Tokyo and Ulaanbaatar; both of them can be used to correct the errors
in the top part of the original one. The statistical error information is used because these images do not
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belong to the database. In the previous methods,
the image from Ulaanbaatar with smaller number of
the correct lines in the overlapped area might be selected and probably not all of errors are corrected.
With proposed method, based on the statistical information, because the number of correct lines in the
overlapped area of the image from Tokyo is greater, it
is selected to make sure that more errors will be corrected. After correction, all the errors in the top part
of the original image are corrected. The real error
information of the images from Bangkok and Tokyo
is then added to the database.
In figure 9, the original image from Tokyo has two
reference images from Bangkok and Ulaanbaatar. Because these images do not belong to the database,
the statistical error information is used. Based on
this information, the image from Bangkok is selected
to correct the error at the bottom part of the original image; the image from Ulaanbaatar is selected to
correct the error at the top part of the original image. After correction, all errors are corrected. The
real error information of these images is also added
to the database. Compared to the previous methods,
the processing time of proposed method is shorter
because it does not need to analyze the error information of the reference images before selection.
References
[1]
[2]
[3]
[4]
[5]
[6]
Paul M. Mather, Computer Processing of
Remotely-Sensed Images, John Wiley and Sons,
Inc., England, Third Edition, 2004, ch. 2.
K. Yamauchi, M. Takagi, “Quality inspection
and error correction of NOAA data, and its application to the generation of Asian mosaic,”
Proceedings of International Symposium on Remote Sensing 2000, pp. 227-233, 2000.
A. N. Van, Y. Aoki, “Error correction for NOAA
AHVRR data using reference data,” Proceedings
of Asian Conference on Remote Sensing 2006
(CDROM, I-11), Ulaanbaatar, Mongolia, 2006.
A. N. Van, Y. Aoki, “Error correction for NOAA
AVHRR data with reference to the same orbital data,” Proceedings of International Workshop on Advanced Image Technology, Bangkok,
Thailand, pp. 298-303, 2007.
A. Ardeshir Goshtasby, 2-D and 3-D Image Registeration for Medical, Remote Sensing, and Industrial Applications, John Wiley and Sons, Inc.,
Hoboken, New Jersey, 2005, ch. 4.
Bernardo Esteves Pires, Perdo M. Q. Aguiar,
“Registration of Images with Small Overlap,”
IEEE 6th Workshop on Multimedia Signal Processing, pp. 255-258, 2004.
7. CONCLUSIONS
A new method to correct missing lines and error
lines in NOAA image is proposed. This method corrects errors by using the reference images that overlap
the original one. The quality of the reference images
is assessed based on the error information database;
therefore, the number errors in the overlapped area of
reference images are minimized. The information in
the database also helps to save the analyzing time for
un-used reference images. The best quality reference
image is used first to correct errors. After correction,
the other ones will be used if there are still errors
in the original image. Missing and error lines are
corrected by assigning the correct values of the corresponding lines from reference images. Consequently,
missing data could be fully restored and error lines
could be precisely corrected. The results proved that
all most all of the errors were corrected within a quite
short time.
At this time, the database is quite small because
it contains only the information of the image three
receiving stations in the short duration of time. In
the future, the information of the images from other
receiving stations will be added to the database. At
that time, an original image will have more reference
images and the result will be better.
The cumulative parameter also has an important
role. When more data is added to the database, more
values of this parameter will be tested and its best
value will be found more accurately.
An Ngoc Van received the BS and MS
degrees on Information Technology and
Communication from Hanoi University
of Technology, Vietnam, in 2000 and
2003, respectively. He is currently a
Ph.D. student at Shibaura Institute of
Technology, Japan. His research interests include speech signal processing and
satellite image processing.
Yoshimitsu Aoki received the BS, MS
and Ph.D. degrees on Applied Physics
from Waseda University, Japan in 1996,
1998 and 2001, respectively.
He is
currently Associate Professor of Department of Information Science and
Engineering, Faculty of Engineering,
Shibaura Institute of Technology, Japan.
He is involved in researches related to
image processing and vision systems
that integrate images and sensory information. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE) and Information Processing Society of Japan (IPSJ).
Precise error correction method for NOAA AVHRR image using the same orbital images
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Fig.7: The first test: All images belong to the database.
Fig.8:
2002.
The second test: All images do not belong to the database but are received from June to October,
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Fig.9: The third test: All images do not belong to the database and are received in 1998
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