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a
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:
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zz
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m
S
atellite
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rticle
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CC B
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SA
li
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se
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C
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A
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:
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Mo
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ica
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s
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Data
C
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ter
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N
atio
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s
titu
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f
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Sp
ac
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,
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L
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n
St.
,
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n
,
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ta
T
im
u
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,
1
3
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1
0
,
I
n
d
o
n
esia
.
E
m
ail:
d
o
n
n
a.
m
o
n
ica@l
ap
an
.
g
o
.
id
1.
I
NT
RO
D
UCT
I
O
N
Satellite im
ag
es,
e
s
p
ec
ially
im
ag
es with
v
er
y
h
ig
h
-
r
eso
l
u
tio
n
,
h
av
e
m
ass
iv
e
in
f
o
r
m
atio
n
c
o
n
tain
ed
in
th
em
.
Ver
y
h
ig
h
-
r
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lu
tio
n
s
atellite
im
ag
es
h
as
a
lo
t
o
f
n
o
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ze
r
o
h
ig
h
-
f
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e
q
u
en
c
y
co
m
p
o
n
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ts
wh
ile
th
e
b
an
d
wid
th
a
n
d
th
e
s
to
r
ag
e
to
tr
an
s
m
it
an
d
s
to
r
e
th
em
i
s
n
o
t
u
n
lim
ited
[1
,
2
]
.
T
h
o
s
e
co
n
s
tr
ain
ts
m
ak
e
it
n
ec
ess
ar
y
to
d
ev
el
o
p
co
m
p
r
e
s
s
io
n
m
eth
o
d
s
f
o
r
s
atellite
im
ag
es.
On
1
9
9
8
,
[
3
]
m
o
d
if
ie
d
th
e
s
tan
d
ar
d
J
PEG
co
d
in
g
to
i
n
cr
ea
s
e
th
e
co
m
p
r
ess
io
n
r
atio
f
o
r
s
atellite
im
ag
es.
Sin
ce
th
e
n
,
r
esear
ch
es
o
n
co
m
p
r
ess
io
n
f
o
r
s
atellite
im
ag
es
h
as
b
ee
n
co
n
d
u
cted
[4
-
7]
.
T
h
e
co
n
s
u
ltativ
e
co
m
m
ittee
f
o
r
s
p
ac
e
d
ata
s
y
s
tem
s
(
C
C
S
DS)
also
d
ev
elo
p
e
d
s
ev
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al
r
ec
o
m
m
e
n
d
ed
s
tan
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s
t
o
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ag
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m
p
r
e
s
s
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f
o
r
r
em
o
te
s
en
s
in
g
im
ag
es
s
u
ch
as
C
C
SDS
1
2
1
.
0
-
B
-
2
[
8
]
an
d
C
C
SDS
1
2
2
.
0
-
B
-
2
[
9
]
.
Alth
o
u
g
h
C
C
SD
S
is
th
e
r
ec
o
m
m
en
d
ed
s
tan
d
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d
,
th
is
m
eth
o
d
s
till
h
av
e
r
o
o
m
f
o
r
im
p
r
o
v
e
m
en
t,a
s
s
h
o
wn
in
[
1
0
,
1
1
]
.
T
h
e
C
C
SDS
m
eth
o
d
an
d
m
an
y
o
th
er
c
o
m
p
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m
eth
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s
co
m
m
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ly
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d
is
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an
s
f
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m
(
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,
a
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m
eth
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ased
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ll
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n
al
f
ir
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t
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tr
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d
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d
i
n
1
9
8
9
[
1
2
]
.
DW
T
in
co
m
p
r
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n
wo
r
k
s
b
y
r
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r
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ts
th
at
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if
f
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ca
les
[
1
3
]
.
I
n
2
0
0
6
,
[
1
4
]
i
n
tr
o
d
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ce
d
a
tr
a
n
s
f
o
r
m
atio
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m
eth
o
d
with
f
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zz
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p
p
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x
im
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,
ca
lled
Fu
zz
y
tr
an
s
f
o
r
m
(
F
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s
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)
.
F
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f
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f
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f
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th
at
tr
an
s
f
o
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m
a
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in
ter
v
al
[
a
,
b
]
in
to
n
-
d
im
en
s
io
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al
m
atr
ix
.
Sev
e
r
a
l
ap
p
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ca
tio
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s
o
f
th
is
m
eth
o
d
in
clu
d
es
s
tatis
tics
s
u
ch
as
f
o
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asti
n
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[
1
5
]
a
n
d
r
o
b
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s
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esti
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ato
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s
[
1
6
]
,
a
n
d
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m
ag
e
p
r
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ce
s
s
in
g
s
u
ch
as
r
est
o
r
atio
n
[
1
7
]
a
n
d
c
o
m
p
r
ess
io
n
[
1
8
]
.
I
n
2
0
0
8
,
[
1
9
]
p
r
o
v
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es a
F
-
tr
a
n
s
f
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b
ased
co
m
p
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o
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n
atu
r
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g
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ey
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c
ale
im
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es.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
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KOM
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T
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m
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C
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p
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u
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tr
a
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s
fo
r
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fo
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h
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h
-
r
eso
lu
tio
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s
a
tellite ima
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es c
o
mp
r
ess
io
n
(
Do
n
n
a
Mo
n
ica
)
1131
I
n
th
is
p
ap
er
,
we
m
o
d
if
y
th
e
F
-
tr
an
s
f
o
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b
y
ch
an
g
in
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m
em
b
er
s
h
ip
f
u
n
c
tio
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s
in
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p
s
eu
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o
-
ex
p
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e
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tial
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d
ap
p
ly
th
e
m
eth
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to
co
m
p
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v
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ig
h
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lu
tio
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im
ag
es
tak
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Pleiad
es
s
atell
ite.
W
e
ev
alu
ate
th
e
r
esu
lts
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ased
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n
th
e
p
ea
k
s
ig
n
al
-
to
-
n
o
is
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r
atio
(
PS
NR
)
v
alu
e
a
n
d
th
e
tim
e
co
n
s
u
m
p
tio
n
.
A
co
m
p
ar
is
o
n
with
th
e
r
ec
o
m
m
en
d
ed
s
tan
d
a
r
d
C
C
SD
S a
n
d
W
av
elet
m
eth
o
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is
also
p
r
o
v
id
e
d
in
o
r
d
er
t
o
o
b
s
er
v
e
wh
eth
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o
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r
p
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p
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s
ed
m
eth
o
d
is
b
etter
th
a
n
th
e
o
r
ig
in
al
F
-
tr
a
n
s
f
o
r
m
an
d
th
e
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ec
o
m
m
en
d
ed
s
tan
d
ar
d
m
eth
o
d
o
r
n
o
t.
T
h
e
p
ap
e
r
is
o
r
g
a
n
ized
in
th
e
f
o
llo
win
g
way
.
Sectio
n
2
d
escr
ib
es
th
e
f
u
zz
y
tr
an
s
f
o
r
m
th
eo
r
y
as
th
e
b
ac
k
g
r
o
u
n
d
f
o
r
th
is
r
esear
ch
.
Sectio
n
3
d
escr
ib
es
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
,
wh
ic
h
is
f
u
zz
y
tr
an
s
f
o
r
m
with
m
o
d
if
ied
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
.
T
h
e
d
ataset
f
o
r
th
is
r
esear
ch
an
d
o
u
r
r
esear
ch
m
eth
o
d
is
d
escr
ib
ed
in
s
ec
tio
n
4
.
Sectio
n
5
s
h
o
ws
th
e
ex
p
e
r
im
en
tal
r
esu
lts
with
s
tatis
t
ical
an
aly
s
is
an
d
co
m
p
ar
is
o
n
with
ex
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tin
g
m
eth
o
d
s
.
T
h
e
co
n
clu
s
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n
s
a
n
d
f
u
tu
r
e
wo
r
k
s
p
r
esen
ted
in
s
ec
tio
n
6
.
2.
F
UZ
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Y
T
RAN
SFO
RM
Fu
zz
y
tr
an
s
f
o
r
m
,
o
r
F
-
tr
an
s
f
o
r
m
,
is
a
m
eth
o
d
o
f
tr
an
s
f
o
r
m
atio
n
in
tr
o
d
u
ce
d
b
y
[
1
4
]
.
Fu
zz
y
t
r
an
s
f
o
r
m
co
m
b
in
es
th
e
co
n
ce
p
t
o
f
cla
s
s
ic
tr
an
s
f
o
r
m
atio
n
m
eth
o
d
s
,
s
u
ch
as
Fo
u
r
ier
tr
an
s
f
o
r
m
,
with
th
e
co
n
ce
p
t
o
f
co
n
d
itio
n
al
(
I
F
-
T
HE
N)
r
u
les
f
o
u
n
d
in
f
u
zz
y
m
o
d
ellin
g
.
I
n
g
en
er
al,
F
-
tr
an
s
f
o
r
m
estab
lis
h
ed
a
co
r
r
esp
o
n
d
en
c
e
b
etwe
en
r
ea
l,
co
n
tin
u
o
u
s
f
u
n
ctio
n
s
an
d
n
-
d
im
en
s
io
n
al
r
ea
l
v
ec
to
r
s
.
F
-
tr
an
s
f
o
r
m
e
n
ab
les
u
s
to
s
o
lv
e
co
m
p
licated
m
ath
em
atica
l
p
r
o
b
lem
s
b
y
s
im
p
lify
in
g
th
e
m
in
to
n
-
d
im
en
s
io
n
al
m
atr
ices
,
an
d
s
o
lv
in
g
th
em
u
s
in
g
s
im
p
le
lin
ea
r
alg
eb
r
a
b
e
f
o
r
e
tr
an
s
f
o
r
m
in
g
th
e
s
o
lu
tio
n
s
b
ac
k
in
to
th
e
p
r
o
b
lem
s
'
o
r
ig
in
al
d
o
m
ain
.
2
.
1
.
M
em
bersh
i
p F
un
ct
io
ns
Me
m
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
th
e
b
asis
o
f
F
-
tr
an
s
f
o
r
m
.
F
-
tr
an
s
f
o
r
m
wo
r
k
s
b
y
cr
ea
tin
g
f
u
z
zy
s
u
b
s
ets
o
f
th
e
d
o
m
ain
u
s
in
g
s
ev
er
al
p
r
e
d
eter
m
in
ed
m
e
m
b
er
s
h
ip
s
f
u
n
ctio
n
s
.
T
h
en
,
a
v
er
ag
e
v
alu
es
o
f
a
f
u
n
ctio
n
o
v
er
th
e
f
u
zz
y
s
u
b
s
ets
ar
e
ca
lcu
lated
,
s
o
th
at
th
e
f
u
n
ctio
n
c
o
u
ld
b
e
m
ap
p
ed
f
r
o
m
th
e
f
u
zz
y
s
u
b
s
ets
to
th
e
av
er
ag
e
f
u
n
ctio
n
v
alu
es
[
1
4
]
.
T
h
er
e
ar
e
m
an
y
f
u
n
ctio
n
s
th
at
co
u
ld
b
e
u
s
ed
as
a
m
em
b
er
s
h
ip
f
u
n
ctio
n
,
s
u
c
h
as
tr
ian
g
u
lar
f
u
n
ctio
n
[
2
0
]
,
tr
ap
ez
o
id
f
u
n
ctio
n
[
2
1
]
,
an
d
g
a
u
s
s
f
u
n
ctio
n
[
2
2
]
.
C
o
m
m
o
n
f
u
n
ctio
n
u
s
ed
f
o
r
F
-
tr
an
s
f
o
r
m
'
s
m
em
b
er
s
h
ip
f
u
n
ctio
n
is
s
in
u
s
o
id
al
f
u
n
ctio
n
[
1
9
,
23]
.
T
h
e
(
1
)
s
h
o
ws
th
e
f
o
r
m
al
ex
p
r
ess
io
n
f
o
r
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
cti
o
n
s
1
(
)
,
(
)
,
(
)
wh
en
=
2
,
…
,
−
1
.
1
(
)
=
{
0
.
5
(
c
os
ℎ
(
−
1
)
+
1
)
f
o
r
∈
[
1
,
2
]
0
f
o
r
o
th
er
wis
e
(
1
)
(
)
=
{
0
.
5
(
c
os
ℎ
(
−
)
+
1
)
f
o
r
∈
[
−
1
,
+
1
]
0
f
o
r
o
th
er
wis
e
(
)
=
{
0
.
5
(
c
os
ℎ
(
−
)
+
1
)
f
o
r
∈
[
−
1
,
]
0
f
o
r
o
th
er
wis
e
2
.
2
.
Dire
ct
a
nd
I
nv
er
s
e
F
-
t
ra
ns
f
o
rm
F
-
tr
an
s
f
o
r
m
estab
lis
h
es
a
co
r
r
esp
o
n
d
en
c
e
b
etwe
en
a
s
et
o
f
co
n
tin
u
o
u
s
f
u
n
ctio
n
s
o
n
in
te
r
v
al
[
a
,
b
]
an
d
a
s
et
o
f
n
-
d
im
en
s
io
n
al
v
ec
to
r
s
.
Su
p
p
o
s
ed
A
1
,
.
.
.
,
A
n
a
r
e
m
em
b
er
s
h
ip
s
f
u
n
cti
o
n
s
u
s
ed
to
cr
ea
te
f
u
zz
y
s
u
b
s
ets
f
r
o
m
d
o
m
ai
n
[
a
,
b
]
,
a
n
d
f
is
an
y
f
u
n
ctio
n
f
r
o
m
th
e
s
et
o
f
co
n
tin
u
o
u
s
f
u
n
ctio
n
s
o
n
in
ter
v
al
[
a
,
b
]
.
T
h
e
n
-
tu
p
le
o
f
r
ea
l n
u
m
b
er
s
[F
1
, ..., F
n
]
g
iv
e
n
b
y
(
2
)
.
is
th
e
F
-
tr
an
s
f
o
r
m
o
f
f
with
r
esp
ec
t t
o
A
1
,
.
.
.
,
A
n
[
1
4
]
.
=
∫
(
)
(
)
∫
(
)
,
=
1
,
…
,
(
2
)
T
h
is
p
ap
er
u
s
es
th
e
s
lig
h
tly
m
o
d
if
ied
F
-
tr
an
s
f
o
r
m
b
y
[
1
9
]
to
m
a
k
e
it
co
m
p
atib
le
w
ith
im
ag
e
co
m
p
r
ess
io
n
,
n
am
ely
d
is
cr
ete,
two
-
v
ar
iab
le
F
-
tr
an
s
f
o
r
m
w
ith
d
ef
in
itio
n
as
f
o
llo
w:
Su
p
p
o
s
ed
R
is
a
g
r
ey
im
ag
e
o
f
N
x
M
p
ix
els.
T
h
e
n
o
r
m
alize
d
v
alu
e
o
f
th
e
p
ix
el,
d
en
o
ted
R
(
i,j)
,
ca
n
b
e
s
ee
n
as
f
u
zz
y
r
elat
io
n
R
:
(
i,j)
∈
[
1
,
.
.
.
,
N
]
×
[
1
,
.
.
.
,
M]
→
[
0
,
1
]
.
T
h
e
co
m
p
r
ess
io
n
o
f
R
is
d
o
n
e
b
y
d
is
cr
ete
F
-
tr
a
n
s
f
o
r
m
with
two
v
ar
iab
le
s
[F
kl
]
g
iv
en
b
y
:
=
∑
∑
(
,
)
(
)
(
)
=
1
=
1
∑
∑
(
)
(
)
=
1
=
1
(
3
)
wh
er
e
A
1
,
.
.
.
,
A
n
d
en
o
tes
m
em
b
er
s
h
ip
s
f
u
n
ctio
n
s
u
s
ed
to
cr
ea
te
f
u
zz
y
s
u
b
s
ets
f
r
o
m
d
o
m
ain
[
1
,
N
]
,
wh
ile
B
d
en
o
tes
m
em
b
e
r
s
h
ip
s
f
u
n
ctio
n
s
u
s
ed
t
o
cr
ea
te
f
u
zz
y
s
u
b
s
ets
f
r
o
m
d
o
m
ain
[
1
,
M]
.
Ma
tr
i
x
F
kl
cr
ea
ted
b
y
(
3
).
ca
n
b
e
tr
an
s
f
o
r
m
ed
b
ac
k
i
n
to
t
h
e
o
r
ig
in
al
d
o
m
ain
u
s
in
g
in
v
er
s
e
F
-
tr
an
s
f
o
r
m
ex
p
r
ess
ed
as f
o
llo
w:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
2
,
Ap
r
il 2
0
2
0
:
1
1
3
0
-
1
1
3
6
1132
(
)
(
)
(
,
)
=
∑
∑
(
)
(
)
(
)
=
1
(
)
=
1
(
4
)
wh
er
e
(
)
(
)
ap
p
r
o
x
im
ates th
e
o
r
ig
i
n
al
b
lo
ck
R
B
[
1
9
]
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
is
p
ap
er
u
s
e
p
s
eu
d
o
-
e
x
p
o
n
en
tial
f
u
n
ctio
n
[
2
4
]
in
s
tead
o
f
s
in
u
s
o
id
al
f
u
n
ctio
n
as
m
em
b
er
s
h
ip
f
u
n
ctio
n
f
o
r
th
e
F
-
tr
an
s
f
o
r
m
.
Ps
eu
d
o
-
ex
p
o
n
e
n
tial
f
u
n
ctio
n
is
a
b
ell
-
s
h
ap
ed
f
u
n
ctio
n
d
e
f
in
ed
b
y
its
ce
n
ter
v
alu
e
m
an
d
a
v
alu
e
k
<
1
.
As
th
e
v
alu
e
k
in
c
r
ea
s
es,
g
r
o
wth
r
ate
also
in
cr
ea
s
es,
m
ak
in
g
th
e
b
ell
m
o
r
e
n
ar
r
o
w
.
T
h
e
f
u
n
ctio
n
'
s
f
o
r
m
al
ex
p
r
ess
io
n
ca
n
b
e
s
ee
n
in
(
5
)
.
(
)
=
1
1
+
(
−
)
2
(
5
)
An
illu
s
tr
atio
n
f
o
r
p
s
eu
d
o
-
ex
p
o
n
en
tial f
u
n
ctio
n
with
=
5
an
d
=
0
.
5
is
s
h
o
wn
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Sam
p
le
g
r
ap
h
o
f
p
s
eu
d
o
-
e
x
p
o
n
en
tial
f
u
n
ctio
n
with
=
5
an
d
=
0
.
5
W
e
u
s
e
F
-
tr
an
s
f
o
r
m
with
p
s
eu
d
o
-
e
x
p
o
n
en
tial
f
u
n
ctio
n
as
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
t
o
co
m
p
r
ess
v
er
y
h
i
g
h
-
r
eso
l
u
tio
n
s
atellite
im
ag
es.
T
h
e
(
6
)
s
h
o
ws
th
e
f
o
r
m
al
ex
p
r
ess
io
n
f
o
r
p
s
eu
d
o
-
e
x
p
o
n
e
n
tial
f
u
n
ctio
n
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
1
(
)
,
(
)
,
(
)
wh
en
=
2
,
…
,
−
1
.
1
(
)
=
{
1
1
+
2
.
5
(
−
(
1
−
ℎ
2
⁄
)
+
(
2
−
ℎ
2
⁄
)
2
)
2
f
o
r
∈
[
1
,
2
]
0
f
o
r
o
th
er
wis
e
(
6
)
(
)
=
{
1
1
+
0
.
9
(
−
−
1
+
+
1
2
)
2
f
o
r
∈
[
−
1
,
+
1
]
0
f
o
r
o
th
er
wis
e
(
)
=
{
1
1
+
2
.
5
(
−
(
−
1
+
ℎ
2
⁄
)
+
(
+
ℎ
2
⁄
)
2
)
2
f
o
r
∈
[
−
1
,
]
0
f
o
r
o
th
er
wis
e
Valu
e
=
2
.
5
,
=
0
.
9
,
as we
ll a
s
v
alu
es f
o
r
ar
e
ch
o
s
en
f
r
o
m
s
ev
er
al
tr
ials
an
d
er
r
o
r
s
s
o
th
at
th
e
f
u
n
ctio
n
s
1
,
…,
f
o
llo
ws t
h
e
p
r
o
p
er
ties
o
f
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
as d
ef
i
n
ed
in
[
1
4
]
.
4.
RE
S
E
ARCH
M
E
T
H
O
D
W
e
u
s
e
F
-
tr
an
s
f
o
r
m
with
p
s
eu
d
o
-
e
x
p
o
n
en
tial
f
u
n
ctio
n
as
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
t
o
co
m
p
r
ess
v
er
y
h
ig
h
-
r
eso
lu
tio
n
s
atellite
im
ag
es.
T
h
e
ex
p
e
r
im
en
t
is
co
n
d
u
cted
i
n
th
e
f
o
llo
win
g
way
.
First
we
b
r
ea
k
d
o
wn
th
e
im
ag
es
in
t
o
8
x
8
tiles
an
d
n
o
r
m
alize
th
e
p
ix
el'
s
v
alu
es
o
f
ea
ch
tile
in
to
[
0
,
1
]
in
ter
v
al.
E
ac
h
in
ter
v
al
is
th
en
tr
an
s
f
o
r
m
e
d
u
s
in
g
F
-
t
r
an
s
f
o
r
m
eq
u
atio
n
in
to
n
-
d
im
en
s
io
n
al
m
atr
ices.
T
h
e
m
a
tr
ices
ar
e
th
en
r
ec
o
n
s
tr
u
cted
in
to
n
ew,
co
m
p
r
ess
ed
im
ag
es
u
s
in
g
in
v
er
s
e
F
-
tr
an
s
f
o
r
m
.
Fo
r
co
m
p
ar
is
o
n
,
we
also
co
n
d
u
ct
co
m
p
r
ess
io
n
u
s
in
g
s
ev
er
al
m
eth
o
d
s
,
wh
ich
ar
e
th
e
o
r
i
g
in
al
F
-
tr
an
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
[
1
4
]
,
C
C
SDS
m
eth
o
d
[
2
5
]
,
an
d
W
av
elet
m
eth
o
d
(
h
ttp
s
://g
ith
u
b
.
co
m
/g
p
ey
r
e/
m
atlab
-
to
o
lb
o
x
es
)
.
T
h
e
r
esu
lts
ar
e
th
en
e
v
alu
ated
b
y
th
eir
Peak
Sig
n
al
-
to
-
No
is
e
R
atio
(
PS
N
R
)
v
alu
e
an
d
tim
e
co
n
s
u
m
p
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
u
z
z
y
tr
a
n
s
fo
r
m
fo
r
h
ig
h
-
r
eso
lu
tio
n
s
a
tellite ima
g
es c
o
mp
r
ess
io
n
(
Do
n
n
a
Mo
n
ica
)
1133
PS
NR
i
s
a
m
eth
o
d
c
o
m
m
o
n
ly
u
s
ed
f
o
r
ev
al
u
atin
g
im
a
g
e
an
d
v
id
e
o
p
r
o
ce
s
s
in
g
[
1
,
1
9
]
.
As
th
e
n
am
e
im
p
lies
,
PS
N
R
is
a
r
atio
b
etwe
en
th
e
m
ax
im
u
m
v
al
u
e
o
f
a
s
ig
n
al
an
d
a
n
o
is
e
d
i
s
tu
r
b
in
g
th
e
s
ig
n
al
r
ep
r
esen
tatio
n
,
ca
lcu
lated
u
s
in
g
m
ea
n
s
q
u
a
r
ed
er
r
o
r
(
MSE
)
.
T
h
e
lo
wer
th
e
n
o
is
e
a
n
d
th
u
s
th
e
MSE
v
alu
e
,
th
e
h
ig
h
er
th
e
PS
NR
v
alu
e
an
d
th
e
im
ag
e
q
u
ality
[
2
6
]
.
T
h
e
eq
u
atio
n
f
o
r
PS
NR
is
as f
o
llo
wed
[
2
7
]
:
PS
NR
=
20
×
l
og
10
2
1
∑
2
(
,
)
−
1
=
1
(
7
)
wh
er
e
Q
is
th
e
p
o
s
s
ib
le
m
ax
im
u
m
v
alu
e
o
f
th
e
p
ix
el,
N
is
th
e
n
u
m
b
er
o
f
p
ix
els
o
f
th
e
im
ag
e,
an
d
u
is
th
e
v
alu
e
o
f
p
ix
el.
T
h
e
d
ataset
u
s
ed
in
th
is
p
ap
er
is
v
er
y
h
ig
h
-
r
eso
l
u
tio
n
im
ag
es
,
th
at
ar
e
im
ag
es
with
s
p
atial
r
eso
lu
tio
n
u
n
d
er
o
n
e
m
ete
r
,
tak
en
f
r
o
m
Pleiad
es
c
o
n
s
tellatio
n
s
atellite
s
.
Pleiad
es
s
atel
lites
p
r
o
d
u
ce
m
u
ltis
p
ec
tr
al
im
ag
es
wh
ich
co
n
s
is
t
o
f
f
o
u
r
b
an
d
s
:
r
ed
,
g
r
ee
n
,
b
lu
e,
an
d
n
ea
r
in
f
r
ar
ed
.
Fo
r
th
e
ex
p
e
r
im
en
t,
w
e
u
s
e
n
atu
r
al
co
lo
r
co
m
b
in
atio
n
(
r
e
d
,
g
r
ee
n
,
b
lu
e
)
with
0
.
5
m
s
p
atial
r
eso
lu
tio
n
s
to
r
ed
in
1
6
-
bi
t.
Fig
u
r
e
2
s
h
o
ws
s
ev
er
al
s
am
p
les
o
f
th
e
d
ataset.
(
a)
(
b
)
(
c)
(
d
)
(
e)
(f)
F
ig
u
r
e
2
.
Sam
p
le
im
a
g
es f
r
o
m
p
leiad
es satellit
e,
ea
ch
im
ag
e
is
5
1
2
×
5
1
2
p
ix
el
5.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
e
co
m
p
r
ess
io
n
r
esu
lts
u
n
d
er
F
-
tr
an
s
f
o
r
m
m
et
h
o
d
wi
th
p
s
eu
d
o
-
ex
p
o
n
e
n
tial
an
d
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
,
C
C
S
DS
m
eth
o
d
,
an
d
W
av
elet
m
eth
o
d
ar
e
p
r
esen
ted
in
T
a
b
le
1
an
d
T
ab
le
2
.
C
o
m
p
ar
is
o
n
f
o
r
t
h
e
PS
NR
v
al
u
e
is
p
r
esen
ted
in
Fig
u
r
e
3
.
I
t
ca
n
b
e
s
ee
n
th
at
f
o
r
e
v
er
y
c
o
m
p
r
ess
io
n
r
atio
we
test
ed
,
th
e
PS
NR
v
alu
es
o
f
co
m
p
r
ess
ed
im
ag
es
b
y
F
-
tr
an
s
f
o
r
m
,
b
o
th
u
s
in
g
p
s
eu
d
o
-
ex
p
o
n
e
n
tial
an
d
s
in
u
s
o
id
al
f
u
n
ctio
n
a
r
e
s
ig
n
if
ican
tly
h
ig
h
er
th
at
th
o
s
e
o
f
C
SDS
m
eth
o
d
an
d
W
av
elet
m
eth
o
d
.
T
h
e
PS
NR
v
alu
e
f
o
r
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
1
9
.
8
3
%
h
ig
h
er
th
at
th
e
co
m
p
r
ess
ed
im
ag
es
u
s
in
g
th
e
r
ec
o
m
m
en
d
e
d
s
tan
d
ar
d
C
C
SDS
,
an
d
5
0
.
7
6
%
h
ig
h
er
th
an
W
a
v
elet
m
eth
o
d
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
'
s
PS
N
R
v
alu
e
is
o
n
l
y
2
.
0
7
%
lo
wer
th
a
n
PS
NR
v
alu
e
o
f
F
-
tr
an
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
.
As
h
ig
h
er
PS
NR
v
alu
e
m
ea
n
s
m
o
r
e
s
im
ilar
ity
b
etwe
en
o
r
ig
in
al
i
m
ag
e
an
d
co
m
p
r
ess
ed
im
ag
e
,
th
e
r
esu
lts
s
h
o
w
th
at
im
ag
es
co
m
p
r
ess
ed
with
th
e
p
r
o
p
o
s
ed
m
eth
o
d
ar
e
a
b
le
to
r
etain
m
o
r
e
in
f
o
r
m
atio
n
an
d
th
u
s
b
etter
th
an
o
n
es
co
m
p
r
ess
ed
with
th
e
r
ec
o
m
m
e
n
d
e
d
s
tan
d
a
r
d
C
C
SDS
an
d
W
av
elet
m
eth
o
d
,
wh
ile
o
n
ly
s
lig
h
tly
in
f
er
i
o
r
t
o
F
-
tr
an
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ct
io
n
.
T
h
e
co
m
p
a
r
is
o
n
o
f
v
is
u
al
q
u
ality
o
f
th
e
co
m
p
r
ess
ed
im
ag
es
is
s
h
o
w
in
Fig
u
r
e
4
.
I
n
ter
m
o
f
tim
e
co
n
s
u
m
p
tio
n
,
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
h
as h
ig
h
e
s
t c
o
m
p
lex
ity
with
av
er
ag
e
ti
m
e
n
ee
d
ed
to
co
m
p
r
ess
o
n
e
im
ag
e
1
8
7
.
1
9
5
4
s
ec
o
n
d
s
.
T
h
e
s
ec
o
n
d
h
ig
h
est
co
m
p
lex
ity
g
o
es
to
F
-
tr
an
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
with
av
er
ag
e
co
n
s
u
m
p
tio
n
tim
e
9
2
.
0
5
0
5
s
ec
o
n
d
s
,
f
o
llo
wed
b
y
W
av
elet
m
eth
o
d
with
av
er
ag
e
c
o
n
s
u
m
p
tio
n
t
im
e
7
7
.
5
4
2
7
s
ec
o
n
d
s
.
Fin
ally
,
C
C
SDS
h
as
lo
west
co
m
p
lex
ity
with
av
er
ag
e
tim
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
2
,
Ap
r
il 2
0
2
0
:
1
1
3
0
-
1
1
3
6
1134
co
n
s
u
m
p
tio
n
o
n
ly
5
.
9
4
8
4
s
ec
o
n
d
s
.
C
o
m
p
ar
is
o
n
f
o
r
th
e
tim
e
co
n
s
u
m
p
tio
n
f
o
r
ea
ch
m
eth
o
d
is
p
r
esen
ted
i
n
Fig
u
r
e
5
.
T
h
e
tim
e
n
ee
d
ed
t
o
co
m
p
r
ess
o
n
e
im
a
g
e
is
1
0
3
.
3
6
%
lo
n
g
er
th
an
F
-
tr
a
n
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
,
1
4
1
.
4
1
% lo
n
g
er
th
a
n
W
av
elet
m
eth
o
d
,
an
d
3
0
0
0
.
9
9
% lo
n
g
er
th
an
C
C
SDS m
eth
o
d
.
T
ab
le
1
.
PS
NR
v
alu
e
co
m
p
a
r
is
o
n
I
mag
e
P
r
o
p
o
se
d
m
e
t
h
o
d
F
TR
S
i
n
u
so
i
d
a
l
C
C
S
D
S
W
a
v
e
l
e
t
a
6
4
.
2
2
4
7
6
5
.
5
2
6
2
4
9
.
7
5
9
8
3
8
.
9
2
1
6
b
6
1
.
1
0
8
8
6
2
.
3
2
8
3
5
1
.
6
5
4
6
4
1
.
2
7
6
9
c
6
0
.
0
7
1
6
6
1
.
3
0
4
8
5
1
.
3
0
0
7
4
0
.
9
5
3
9
d
6
2
.
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2
6
6
6
3
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3
7
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6
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3
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6
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3
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6
5
.
7
9
1
3
6
7
.
1
7
5
5
4
.
1
6
3
2
4
2
.
5
2
9
5
n
6
1
.
1
8
3
3
6
2
.
5
1
4
5
1
.
9
1
5
4
1
.
3
9
7
5
o
6
1
.
4
2
0
5
6
2
.
7
4
4
4
5
1
.
4
3
9
4
0
.
7
9
3
7
p
6
1
.
6
7
7
2
6
2
.
9
7
9
4
9
.
7
3
9
3
3
9
.
1
7
7
2
A
v
e
r
a
g
e
6
1
.
5
2
3
2
1
6
2
.
8
2
4
9
9
5
1
.
3
4
1
3
8
4
0
.
8
0
9
2
3
T
ab
le
2
.
T
im
e
c
o
n
s
u
m
p
tio
n
co
m
p
ar
is
o
n
I
mag
e
P
r
o
p
o
se
d
m
e
t
h
o
d
F
TR
S
i
n
u
so
i
d
a
l
C
C
S
D
S
W
a
v
e
l
e
t
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v
e
r
a
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1
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1
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
o
f
PS
NR
v
alu
es
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
u
z
z
y
tr
a
n
s
fo
r
m
fo
r
h
ig
h
-
r
eso
lu
tio
n
s
a
tellite ima
g
es c
o
mp
r
ess
io
n
(
Do
n
n
a
Mo
n
ica
)
1135
Or
ig
in
al
im
ag
e
Pro
p
o
s
ed
m
eth
o
d
FTR Sin
u
s
o
id
al
C
C
SDS
W
av
elet
Fig
u
r
e
4
.
Vis
u
al
co
m
p
ar
is
o
n
b
etwe
en
F
-
tr
an
s
f
o
r
m
,
W
av
elet,
an
d
C
C
SDS m
eth
o
d
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
tim
e
co
n
s
u
m
p
tio
n
T
h
e
m
ain
ca
u
s
e
f
o
r
th
e
p
r
o
p
o
s
ed
m
eth
o
d
'
s
co
s
tly
tim
e
co
n
s
u
m
p
tio
n
is
th
e
p
r
e
p
r
o
ce
s
s
in
g
th
at
h
ap
p
en
e
d
b
ef
o
r
e
th
e
ac
tu
al
co
m
p
r
ess
in
g
.
As
ex
p
lain
ed
in
Sectio
n
2
,
b
ef
o
r
e
a
p
p
ly
in
g
F
-
tr
an
s
f
o
r
m
to
th
e
im
ag
e,
we
f
ir
s
t
b
r
ea
k
d
o
w
n
th
e
im
ag
e
in
to
8
×
8
-
p
ix
el
tiles
an
d
n
o
r
m
alize
th
e
p
ix
el'
s
v
alu
e
in
to
[
0
,
1
]
in
ter
v
al.
Af
ter
tr
an
s
f
o
r
m
atio
n
,
we
d
en
o
r
m
alize
th
e
p
ix
el'
s
v
alu
e
an
d
m
er
g
e
th
e
tiles
b
ac
k
in
to
s
in
g
le
im
a
g
e.
T
h
is
ad
d
itio
n
al
p
r
o
ce
s
s
also
ad
d
th
e
tim
e
c
o
n
s
u
m
p
tio
n
.
6.
CO
NCLU
SI
O
N
Fro
m
th
e
e
x
p
er
im
en
tal
r
esu
l
ts
,
we
co
n
clu
d
e
th
at
v
er
y
h
ig
h
-
r
eso
lu
tio
n
s
atellite
im
ag
es
ca
n
b
e
co
m
p
r
ess
ed
b
y
F
-
tr
an
s
f
o
r
m
w
ith
p
s
eu
d
o
-
ex
p
o
n
en
tial
f
u
n
cti
o
n
as
th
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
.
T
h
e
co
m
p
r
ess
ed
im
ag
es
h
av
e
v
is
u
ally
g
o
o
d
q
u
ality
,
an
d
c
o
u
ld
r
etain
m
o
s
t
o
f
th
e
in
f
o
r
m
atio
n
as
s
h
o
wn
b
y
th
e
PS
NR
v
alu
es
wh
ich
r
an
g
in
g
ar
o
u
n
d
5
9
-
6
6
d
B
.
T
h
is
r
esu
lt
is
b
etter
th
an
th
e
r
e
co
m
m
e
n
d
ed
s
tan
d
a
r
d
C
C
SDS
an
d
W
av
elet
m
eth
o
d
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
'
s
PS
N
R
v
alu
e
is
o
n
ly
s
lig
h
tly
in
f
er
io
r
to
PS
NR
v
alu
e
o
f
F
-
tr
an
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
i
p
f
u
n
c
tio
n
.
Ho
wev
e
r
,
th
e
p
r
o
p
o
s
e
d
m
eth
o
d
is
s
till
in
f
er
i
o
r
i
n
r
eg
a
r
d
t
o
tim
e
co
n
s
u
m
p
tio
n
.
T
h
e
tim
e
n
ee
d
ed
to
c
o
m
p
r
ess
o
n
e
im
a
g
e
is
m
u
ch
lo
n
g
er
th
a
n
F
-
tr
a
n
s
f
o
r
m
with
s
in
u
s
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
,
th
e
W
av
elet
m
eth
o
d
,
an
d
th
e
C
C
SDS
m
eth
o
d
.
Fo
r
th
e
f
u
tu
r
e
wo
r
k
s
,
we
will
attem
p
t
to
less
en
th
e
tim
e
co
n
s
u
m
p
tio
n
n
ee
d
ed
to
im
ag
e
co
m
p
r
ess
io
n
b
y
lo
wer
in
g
th
e
c
o
m
p
lex
ity
o
f
th
e
co
d
in
g
a
n
d
ex
p
er
im
en
tin
g
o
th
e
r
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
2
,
Ap
r
il 2
0
2
0
:
1
1
3
0
-
1
1
3
6
1136
ACK
NO
WL
E
DG
M
E
N
T
T
h
is
r
esear
ch
is
s
u
p
p
o
r
ted
b
y
I
NSI
NAS
R
e
s
ea
r
ch
Gr
an
t
f
r
o
m
I
n
d
o
n
esian
Min
is
tr
y
o
f
R
esear
ch
,
T
ec
h
n
o
lo
g
y
,
a
n
d
Hig
h
e
r
E
d
u
c
atio
n
with
Gr
an
t n
u
m
b
er
1
1
/I
NS
-
1
/PP
K/E
4
/2
0
1
8
.
RE
F
E
R
E
NC
E
S
[1
]
B.
Li
,
e
t
a
l.
,
“
Re
m
o
te
-
S
e
n
sin
g
I
m
a
g
e
Co
m
p
re
ss
io
n
Us
in
g
Two
-
Dim
e
n
sio
n
a
l
Orie
n
ted
Wav
e
let
T
ra
n
sfo
rm
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
Ge
o
sc
i
e
n
c
e
a
n
d
Rem
o
te S
e
n
s
i
n
g
,
v
o
l.
4
9
,
n
o
.
1
,
p
p
.
2
3
6
-
2
5
0
,
Ja
n
u
a
ry
2
0
1
1
.
[2
]
B.
J.
Ba
b
b
,
e
t
a
l.
,
“
Im
p
ro
v
e
d
sa
t
e
ll
it
e
ima
g
e
c
o
m
p
re
ss
io
n
a
n
d
re
c
o
n
stru
c
t
io
n
v
ia
g
e
n
e
ti
c
a
lg
o
rit
h
m
s,”
Pro
c
e
e
d
in
g
S
PIE
7
1
1
4
,
El
e
c
tro
-
Op
t
ica
l
Rem
o
te S
e
n
sin
g
,
P
h
o
to
n
ic T
e
c
h
n
o
l
o
g
ie
s,
a
n
d
A
p
p
l
ica
ti
o
n
s II
,
2
0
0
8
.
[3
]
P
.
Ho
u
,
e
t
a
l.
,
“
Im
p
ro
v
e
d
JPE
G
c
o
d
in
g
f
o
r
re
m
o
te
se
n
sin
g
,
”
P
ro
c
e
e
d
in
g
1
4
th
In
tern
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
o
n
P
a
tt
e
r
n
Re
c
o
g
n
it
i
o
n
,
v
o
l.
2
,
p
p
.
1
6
3
7
-
1
6
3
9
,
1
9
9
8
.
[4
]
T.
Alg
ra
,
“
Da
ta
c
o
m
p
re
ss
io
n
fo
r
o
p
e
ra
ti
o
n
a
l
S
AR
m
issio
n
s
u
si
n
g
e
n
tro
p
y
-
c
o
n
stra
in
e
d
b
lo
c
k
a
d
a
p
ti
v
e
q
u
a
n
ti
sa
ti
o
n
,
”
IEE
E
In
ter
n
a
ti
o
n
a
l
Ge
o
sc
ien
c
e
a
n
d
Rem
o
te S
e
n
si
n
g
S
y
mp
o
siu
m
,
v
o
l.
2
,
p
p
.
1
1
3
5
-
1
1
3
9
,
2
0
0
2
.
[5
]
Y.
S
u
i
,
e
t
a
l.
,
“
A
l
o
ss
les
s
c
o
m
p
re
ss
io
n
a
lg
o
rit
h
m
o
f
r
e
m
o
te
se
n
sin
g
ima
g
e
fo
r
sp
a
c
e
a
p
p
l
ica
ti
o
n
s,”
J
o
u
r
n
a
l
o
f
El
e
c
tro
n
ics
,
v
o
l.
2
5
,
n
o
.
5
,
p
p
.
6
4
7
-
6
5
1
,
2
0
0
8
.
[6
]
G
.
Yu
,
e
t
a
l.
,
“
F
P
G
A
-
b
a
se
d
o
n
-
b
o
a
rd
m
u
l
ti
/h
y
p
e
rsp
e
c
tral
ima
g
e
c
o
m
p
re
ss
io
n
sy
ste
m
,
”
2
0
0
9
IEE
E
In
ter
n
a
t
io
n
a
l
Ge
o
sc
ien
c
e
a
n
d
Rem
o
te S
e
n
si
n
g
S
y
mp
o
si
u
m
,
p
p
.
V
-
2
1
2
-
V
-
2
1
5
,
2
0
0
9
.
[7
]
M
.
Na
k
a
ji
m
a
,
e
t
a
l.
,
“
Co
m
p
re
ss
io
n
-
b
a
se
d
se
m
a
n
ti
c
-
se
n
siti
v
e
i
m
a
g
e
se
g
m
e
n
tatio
n
:
P
RDC
-
S
S
I
S
,
”
2
0
1
2
IE
EE
In
ter
n
a
t
io
n
a
l
Ge
o
sc
ien
c
e
a
n
d
Re
mo
te S
e
n
si
n
g
S
y
mp
o
si
u
m
,
p
p
.
4
3
0
3
-
4
3
0
6
,
2
0
1
2
.
[8
]
CCS
DS,
“
Lo
ss
les
s
Da
ta
Co
m
p
r
e
ss
io
n
Re
c
o
m
m
e
n
d
e
d
S
tan
d
a
r
d
CCS
DS
1
2
1
.
0
-
B
-
2
,”
Was
h
i
n
g
t
o
n
DC:
CCS
DS
,
M
a
y
2
0
1
2
.
[9
]
CCS
DS,
“
Im
a
g
e
Da
ta
C
o
m
p
re
ss
io
n
Re
c
o
m
m
e
n
d
e
d
S
tan
d
a
r
d
C
CS
DS
1
2
2
.
0
-
B
-
2
,”
Was
h
in
g
to
n
DC:
CCS
D
S
,
S
e
p
tem
b
e
r
2
0
1
7
.
[1
0
]
S.
C.
Tai,
e
t
a
l.
,
“
A Ne
a
r
-
Lo
ss
les
s
Co
m
p
re
ss
io
n
M
e
t
h
o
d
Ba
se
d
o
n
CCS
DS
fo
r
S
a
telli
te Im
a
g
e
s,”
2
0
1
2
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m o
n
Co
m
p
u
ter
,
C
o
n
s
u
me
r a
n
d
C
o
n
tr
o
l
,
p
p
.
7
0
6
-
7
0
9
,
2
0
1
2
.
[1
1
]
E.
Au
g
é
,
e
t
a
l.
,
“
P
e
rfo
rm
a
n
c
e
imp
a
c
t
o
f
p
a
ra
m
e
ter
tu
n
in
g
o
n
t
h
e
CCS
DS
-
1
2
3
l
o
ss
les
s
m
u
lt
i
-
a
n
d
h
y
p
e
rsp
e
c
tral
ima
g
e
c
o
m
p
re
ss
io
n
sta
n
d
a
rd
,
”
J
o
u
rn
a
l
o
f
Ap
p
l
ied
Rem
o
te S
e
n
s
in
g
,
v
o
l
.
7
,
n
o
.
1
,
p
p
.
0
7
4
5
9
4
,
A
u
g
u
st
2
0
1
3
.
[1
2
]
S.
G
.
M
a
ll
a
t
,
“
A
Th
e
o
ry
o
f
M
u
lt
ires
o
l
u
ti
o
n
S
ig
n
a
l
De
c
o
m
p
o
si
t
io
n
:
Th
e
Wav
e
let
Re
p
re
se
n
tatio
n
,”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
Pa
tt
e
rn
A
n
a
l
y
sis
a
n
d
M
a
c
h
i
n
e
In
tell
i
g
e
n
c
e
,
v
o
l.
11,
n
o
.
7
,
p
p
.
6
7
4
-
6
9
3
,
Ju
l
y
1
9
8
9
.
[1
3
]
S
.
M
a
ll
a
t,
“
Zero
Cr
o
ss
in
g
s
o
f
a
Wav
e
let
Tran
sfo
rm
,
”
IEE
E
T
r
a
n
s
a
c
ti
o
n
s
o
n
I
n
f
o
rm
a
ti
o
n
T
h
e
o
ry
,
v
o
l.
3
7
,
n
o
.
4
,
p
p
.
1
0
1
9
-
1
0
3
3
,
Ju
l
y
1
9
9
1
.
[1
4
]
I.
P
e
rfil
ie
v
a
,
“
F
u
z
z
y
tran
sfo
rm
s:
Th
e
o
ry
a
n
d
a
p
p
l
ica
ti
o
n
s,”
Fu
zz
y
S
e
ts
a
n
d
S
y
st
e
ms
,
v
o
l.
1
5
7
,
n
o
.
8
,
p
p
.
9
9
3
-
1
0
2
3
,
Ap
r
il
2
0
0
6
.
[1
5
]
V.
Lo
ia,
e
t
a
l.
,
“
Jo
in
in
g
fu
z
z
y
tr
a
n
sfo
rm
a
n
d
lo
c
a
l
lea
rn
in
g
f
o
r
win
d
p
o
we
r
f
o
re
c
a
stin
g
,
”
2
0
1
7
J
o
in
t
1
7
th
W
o
rl
d
Co
n
g
re
ss
o
f
I
n
ter
n
a
t
io
n
a
l
Fu
zz
y
S
y
ste
ms
Asso
c
ia
ti
o
n
a
n
d
9
t
h
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
S
o
f
t
Co
mp
u
t
in
g
a
n
d
In
telli
g
e
n
t
S
y
ste
ms
(IF
S
A
-
S
CI
S
)
,
p
p
.
1
-
6
,
2
0
1
7
.
[1
6
]
J.
H
.
Yo
o
n
,
e
t
a
l.
,
“
A
h
y
b
ri
d
m
e
th
o
d
b
a
se
d
o
n
F
-
tran
sf
o
rm
fo
r
ro
b
u
st
e
stim
a
to
rs,”
In
t
e
rn
a
t
io
n
a
l
.
J
o
u
rn
a
l
o
f
Ap
p
ro
x
im
a
te
Rea
s
o
n
i
n
g
,
v
o
l.
1
0
4
,
p
p
.
7
5
-
8
3
,
Ja
n
u
a
ry
2
0
1
9
.
[1
7
]
I.
P
e
rfil
iev
a
,
“
F
-
tran
sfo
rm
-
b
a
se
d
o
p
ti
m
iza
ti
o
n
fo
r
ima
g
e
re
st
o
ra
ti
o
n
(i
n
p
a
i
n
ti
n
g
),
”
IEE
E
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
F
u
zz
y
S
y
ste
ms
,
p
p
.
1
-
6,
2
0
1
8
.
[1
8
]
D.
P
a
tern
a
in
,
e
t
a
l.
,
“
Op
ti
m
ize
d
fu
z
z
y
tran
sfo
rm
fo
r
ima
g
e
c
o
m
p
re
ss
io
n
,
”
Ad
v
a
n
c
e
s
in
In
tell
ig
e
n
t
S
y
st
e
ms
a
n
d
Co
mp
u
t
in
g
,
v
o
l
.
6
4
3
,
p
p
.
1
1
8
-
1
2
8
,
2
0
1
8
.
[1
9
]
F
.
Di
M
a
rti
n
o
,
e
t
a
l.
,
“
An
im
a
g
e
c
o
d
in
g
/d
e
c
o
d
i
n
g
m
e
th
o
d
b
a
se
d
o
n
d
irec
t
a
n
d
i
n
v
e
rse
fu
z
z
y
tran
sfo
rm
s,”
In
t
e
rn
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
p
p
r
o
x
i
ma
te
Rea
so
n
in
g
,
v
o
l
.
4
8
,
n
o
.
1
,
p
p
.
1
1
0
-
1
3
1
,
2
0
0
8
.
[2
0
]
Y.
T.
Ju
a
n
g
,
e
t
a
l.
,
“
De
sig
n
o
f
fu
z
z
y
P
ID
c
o
n
tr
o
ll
e
rs
u
sin
g
m
o
d
ifi
e
d
tri
a
n
g
u
lar
m
e
m
b
e
rsh
ip
fu
n
c
ti
o
n
s,”
In
f
o
rm
a
ti
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
1
7
8
,
n
o
.
5
,
p
p
.
1
3
2
5
-
1
3
3
3
,
M
a
r
ch
2
0
0
8
.
[2
1
]
M
.
M
.
J.
Th
e
re
sa
a
n
d
V.
J.
Ra
j,
“
F
u
z
z
y
b
a
se
d
g
e
n
e
t
ic
n
e
u
ra
l
n
e
t
wo
rk
s
fo
r
th
e
c
las
sifica
ti
o
n
o
f
m
u
rd
e
r
c
a
se
s
u
sin
g
Trap
e
z
o
id
a
l
a
n
d
La
g
ra
n
g
e
I
n
te
rp
o
lati
o
n
M
e
m
b
e
rsh
ip
F
u
n
c
ti
o
n
s,”
Ap
p
l
ied
S
o
ft
Co
m
p
u
t
i
n
g
,
v
o
l.
1
3
,
n
o
.
1
,
p
p
.
7
4
3
-
7
5
4
,
Ja
n
u
a
ry
2
0
1
3
.
[2
2
]
C.
Ch
e
n
g
a
n
d
W.
Ba
o
q
ian
g
,
“
A
Traje
c
to
ry
Trac
k
i
n
g
M
e
th
o
d
f
o
r
Weld
i
n
g
M
a
n
ip
u
lato
r
Ba
se
d
o
n
F
u
z
z
y
G
a
u
ss
F
u
n
c
ti
o
n
Ne
u
ra
l
N
e
two
rk
,
”
Pro
c
e
d
ia
En
g
in
e
e
rin
g
,
v
o
l
.
2
9
,
p
p
.
1
8
9
-
1
9
3
,
2
0
1
2
.
[2
3
]
I.
P
e
rfil
ie
v
a
a
n
d
B.
De
Ba
e
ts,
“
F
u
z
z
y
tran
sf
o
rm
s
o
f
m
o
n
o
t
o
n
e
fu
n
c
ti
o
n
s
wit
h
a
p
p
li
c
a
ti
o
n
to
ima
g
e
c
o
m
p
re
ss
io
n
,
”
In
f
o
rm
a
t
io
n
S
c
i
e
n
c
e
s
,
v
o
l
.
1
8
0
,
n
o
.
1
7
,
p
p
.
3
3
0
4
-
3
3
1
5
,
S
e
p
tem
b
e
r
2
0
1
0
.
[2
4
]
J.
G
a
li
n
d
o
,
e
t
a
l.
,
“
F
u
z
z
y
Da
tab
a
se
s:
M
o
d
e
ll
in
g
,
De
sig
n
a
n
d
Im
p
lem
e
n
tatio
n
,”
He
rsh
e
y
,
Id
e
a
G
ro
u
p
P
u
b
l
ish
i
n
g
,
2
0
0
6
.
[2
5
]
A.
In
d
ra
d
jad
,
e
t
a
l.
,
“
A
c
o
m
p
a
riso
n
o
f
S
a
telli
te
Im
a
g
e
Co
m
p
re
ss
io
n
m
e
th
o
d
s
in
th
e
Wav
e
let
Do
m
a
in
,
”
IOP
Co
n
fer
e
n
c
e
S
e
rie
s E
a
rt
h
a
n
d
En
v
iro
n
me
n
t
a
l
S
c
ien
c
e
,
v
o
l.
2
8
0
,
A
u
g
u
st
2
0
1
9
.
[2
6
]
A.
Ho
re
a
n
d
D.
Zi
o
u
,
“
Im
a
g
e
Q
u
a
li
ty
M
e
tri
c
s:
P
S
NR
v
s.
S
S
IM
,
”
2
0
1
0
2
0
th
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Pa
tt
e
rn
Rec
o
g
n
it
io
n
,
p
p
.
2
3
6
6
-
2
3
6
9
,
2
0
1
0
.
[2
7
]
A.
Na
jafip
o
u
r
,
e
t
a
l.
,
“
Co
m
p
a
ri
n
g
th
e
tr
u
stwo
rt
h
in
e
ss
o
f
sig
n
a
l
-
to
-
n
o
ise
ra
ti
o
a
n
d
p
e
a
k
sig
n
a
l
-
to
-
n
o
ise
ra
ti
o
in
p
ro
c
e
ss
in
g
n
o
isy
p
a
rti
a
l
d
isc
h
a
rg
e
sig
n
a
ls,”
IET
S
c
i
e
n
c
e
,
M
e
a
s
u
re
me
n
t,
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
,
n
o
.
2
,
p
p
.
1
1
2
-
1
1
8
,
M
a
r
ch
2
0
1
3
.
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