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2 Communicaiton ,NetworkandAritifcia lIntelilgence(CNA I2018) 8 7 9 : N B S
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a n i h C , 6 7 8 0 0 1 g n ij i e B , s n o it a c i n u m m o c e l e T d n a s t s o P f o y ti s r e v i n U g n ij i e B : s d r o w y e
K Machinel earning ,Deepl earning, Convoluitona lneura lnetwork(CNN) ,Suppor tvector i
h c a
m ne(SVM),Remotesensingi mages, Mulitpleobjectextraciton.
.t c a r t s b
A Thedataprovidedbyremotesensingtechnologyischaracterized bywidecoverage ,high l
a e
r -time performance and a supply of rich and objective informaiton .Therefore ,extracting geo -l p s e g a m i g n i s n e s e t o m e r m o r f s d a o r s a h c u s s t c e j b
o aysimportan trolesinmany urbanappilcations , y ll a u n a m d e m r o f r e p y l l a r e n e g s k s a t h c u s , t s a p e h t n i , r e v e w o H . s a e r a f o e g n a r e d i w a n i d e s u d n a e m i t d n a y l t s o c y r e v s i h c i h
w -consuming .And many ofexistingautomating attemptsaredesigned a e f c i f i c e p s r o
f turesand classifiers wtih ilmitation .This paperproposes a CNN-SVM-based road s e k a t h c i h w , ) s e n i h c a m r o t c e v t r o p p u s e e r h t h ti w k r o w t e n l a r u e n l a n o i t u l o v n o c a ( m e t s y s n o i t c a r t x e e e r h t d e t c i d e r p d n a s t u p n i s a y r e g a m i l a i r e a n i s e u l a v l e x i p w a
r -channe llabe limages(background -s g n i d l i u
b -roads)asoutputs .By training asingleCNN efficiently ,featureextractorsandclassifiers , M V S g n i s U . y l s u o e n a t l u m i s d e t c a r t x e e r a s t c e j b o f o s d n i k e l p i tl u m d n a d e t c u r t s n o c y ll a c i t a m o t u a e r a b n a c s t l u s e r n o i t a c i f i s s a l c e h
t e optimized .A tthe same time , we improve our experimenta l r u o g n i r a p m o c , y l l a n i F . t n e m e c a l p s i d l a i t a p s h t i w g n i t c i d e r p d n a d o h t e m t u o p o r D e h t y b e c n a m r o f r e p N N C f o n o i s i c e r p e h t t a h t t l u s e r e h t t e g e w , s d o h t e m s u o i v e r p h t i w l e d o
m -SVMi ncreases8.37% .
t n
I roduciton
y n a m s a h y r e g a m i l a i r e a m o r f s g n i d li u b d n a s d a o r e k i l s t c e j b o n a b r u e l p i t l u m f o n o i t c a r t x E r e t s a s i d , e c n a s s i a n n o c e r y r a ti l i m , e c a p s o r e a s a h c u s , s d l e i f n a i l i v i c d n a y r a t i l i m n i s n o it a c i l p p a o i t a z il i t u d n a g n i n n a l p d n a l , g n i t s a c e r o f d n a g n i r o t i n o
m n ,etc .However ,duet ot hevariousshapesof s i s e g a m i g n i s n e s e t o m e r e h t f o g n il e b a l l e x i p e t a r u c c a , s r o t c a f e c n e r e f r e t n i e m o s d n a s t c e j b o e s o h t y l h g i h s i s d a o r d n a s g n i d l i u b f o n o it c a r t x e e v it c e f f e , s u h T . e u l a v h c r a e s e r h g i h f o k s a t a l l i t s n a , d e d n a m e
d dt herehavebeenplentyofattemptsutliizingdifferen tmethods. i m e s e r a h c i h w , s e i r o g e t a c o w t o t n i d e i f i s s a l c e b n a c s d o h t e m n o it c a r t x e s t c e j b
O -automaticand
r o f n I . ] 2 [ t o n s e o d r e t t a l e h t e l i h w n o i t c a r e t n i n a m u h s e r i u q e r r e m r o f e h T . ] 1 [ c i t a m o t u a y l l u
f mation
i m e s y b e n o d s e g a m i e t i l l e t a s m o r f n o i t c a r t x
e -automatic methods is usually expensive and time -. d e il p p a d n a d e r o l p x e e r a s d o h t e m c i t a m o t u a , s n o it a ti m i l e s e h t e m o c r e v o o T . g n i m u s n o c u o b a s e v i t c e p s r e p s u o r e m u n , s r e i f i s s a l c d n a s e r u t a e f s u o i r a v n o d e s a
B tautomaticmethodswere W ( n o i t c n u f p i h s r e b m e m d e t h g i e w a y b d e t s i s s a , ) 4 1 0 2 ( l a t e P P . h g n i S n I : d e s o p o r
p -mf),t hefuzzy
-C-means(FCM)techniquesuccessfullyenhancedtheclassification resultsand detected theobjects t c e j b o o w t g n i s U . ] 3
[ -based flitersand suppor tvectormachine (SVM) ,Zelang Miao e ta l(2015) H J g n a W n I . ] 4 [ s s a l c d a o r e h t d e t c a r t x e n e h t d n a s e t a d i d n a c d a o r t c e l e s o t s e r u t a e f t c e j b o d e t u p m o c e g d e l w o n k a d e z i l it u y e h t , ) 6 1 0 2 ( l a t
e -basedmethodtoextrac tthespatialt exturefeatureforimage M K r a m u K , 7 1 0 2 n I . ] 5 [ l e d o m e h t t c u r t s n o c o t s e r u t a e f r e p o r p e r o m t u o d e k r o w d n a n o i t a t n e m g e s i tl u m d i r b y h l e v o n d n a r e tl i f e l c i t r a p g n i s u h c a o r p p a w e n a d e s o p o r p l a t
e -kerne lpartiall eas tsquares . ] 6 [ ) S L P ( s i M V S , s d o h t e m c it a m o t u a e s o h t g n o m
a , s t c e j b o e l p i tl u m h t i w d e t s a r t n o c , n o i t a c i l p p a l a u t c a n
I singleobjec tcanprovidelessinformation e l p i t l u m f o n o i t c a r t x e s u o e n a tl u m i s e h t n o s i s a h p m e e h t t u p e w , e r o f e r e h T . e u l a v e c n e r e f e r e h t d n a n e e b e v a h ) N N C ( k r o w t e n l a r u e n l a n o i t u l o v n o c g n it i o l p x e s e h c a o r p p a , s r a e y t n e c e r n I . s t c e j b o ] 1 1 [ ] 0 1 [ ] 9 [ d e s o p o r
p and applied in solving mutli-objectiveextraction problemswith pretty good l e x i p w a r m o r f s r o t c a r t x e e r u t a e f d o o g t e g d n a s e h c t a p o t n i s e g a m i e d i v i d n a c d o h t e m h c u S . s t c e f f e e r p x e l p m o c t u o h t i w s e u l a
v -processing .In 2016 ,Shunta Saito e ta lgenerated three-channe lmaps s l e x i p g n i d li u b d n a d a o r g n i t c e t e d f o e g a t n a v d a k o o t y e h T . t u p n i y r e g a m i g n i s n e s e t o m e r w a r m o r f n o i t c n u f t u p t u o w e n a d n a y l s u o n o r h c n y s s l e b a l e l p i t l u m g n i r e d i s n o c y r e g a m i g n i s n e s e t o m e r m o r f l e n n a h c d e ll a
c -wisei nhibitedSoftmaxt ot raint heCNN[8] .Ont hebasisoft hepreviouswork ,Rasha w o l l a r e v e s h t i w s e r u t a e f ) N N C ( k r o w t e n l a r u e n l a n o i t u l o v n o c e h t d e n i b m o c ) 7 1 0 2 ( l a t e i h h e h s l A -t s o p e h t n i s g n i d l i u b d n a s d a o r f o s e r u t a e f l e v e
l -processingsectionsoast osmootht hei rregularand j
s i
d oin tregions[12].
e h t f o s e g a t n a v d a n o it c a r t x e t c e j b o e l p i t l u m d n a d e e p s h g i h e h t e n i b m o c e w , s i s e h t e h t n I . l e d o m e h t t c u r t s n o c o t M V S f o s t l u s e r n o i t a c i f i s s a l c t a e r g e h t h t i w k r o w t e n l a r u e n l a n o i t u l o v n o c o t c e v t r o p p u s e s u e w , k r o w s u o i v e r p e h t g n i w o l l o
F rmachine(SVM)totrain theCNNfeaturesand r u O . e c n a m r o f r e p e v o r p m i o t t n e m e c a l p s i d l a i t a p s h t i w g n i t c i d e r p d n a d o h t e m t u o p o r D e h t d e s o p o r p d e w o h s s tl u s e r e h t d n a s t e s a t a d y r e g a m i l a i r e a d a o r s t t e s u h c a s s a M n o d e t c u d n o c e r e w s t n e m i r e p x e e s o p o r p r u o t a h
t d methods outperformed the previous achievements .Our methods avoided the s n i a r t y l t n e d n e p e d n i h c i h w s r e i f i s s a l c f o n g i s e d s u o i r a f i t l u m d n a n o it c a r t x e e r u t a e f d e t a c i l p m o c f i s s a l c y l e t a r u c c a e r o m e r e w s e g a m i n e s o h c r u o n i s l e x i p d n A . d e t c a r t x e e b o t s t c e j b
o ied into
e e r h t , s t n e m i r e p x e r u o n i , s d r o w r e h t o n I . s d a o r d n a , s g n i d l i u b , d n u o r g k c a
b -channe llabe limages
. d e t c u r t s n o c y l t c a x e e r o m e b d l u o c c i l b u p e h t s e c u d o r t n i n o it c e s d r i h t d n a d n o c e s e h T . s w o ll o f s a d e z i n a g r o s i e l c i t r a s i h t f o t s e r e h T t u e w t e s a t a
d iilzed and the environmen tconfiguration including hardwarepar tand softwarepart . r o t c e v t r o p p u s d n a s k r o w t e n l a r u e n n o i t u l o v n o c f o e g d e l w o n k c i s a b e h t s t n e s e r p n o i t c e s h t r u o f e h T s l i a t e d n i e l c i t r a s i h t n i d e s u s d o h t e m e h t e s o p o r p e w , n e h t d n A . e n i h c a
M in thefifth seciton .The e h T . n o i t a u l a v e d n a , n o i t c i d e r p , g n i n i a r t g n i d u l c n i t n e m i r e p x e e h t f o n o i t p i r c s e d a s i n o i t c e s h t x i s l e d o m r u o f o y t i r o i r e p u s e h t d n a y g e t a r t s n o i t a c i f i s s a l c M V S e h t f o n o i s s u c s i d e h t s i n o i t c e s h t n e v e s m e e r h t r e h t o h t i w d e r a p m o
c odels .In thefina lseciton ,wepresen taconclusion for ourimportan t s g n i d n i
f .
D aa S st e t
: e ti s b e w e h t n o h i n M y b d e s o p o r p a t a d e l b a l i a v a y l c i l b u p d e n i a t b o e W / u d e . o t n o r o t. s c . w w w / / : p t t
h ~vmnih/data/ .Merging data from Massachusetts Buildings Datase tand k o o t e W . t e s a t a d s d a o R d n a s g n i d li u B s t t e s u h c a s s a M d e t a e r c e w , t e s a t a D s d a o R s t t e s u h c a s s a M g n i t a l u c l a c y b d e t a e r c e r e w s l e b a l d n u o r g k c a B . s l e n n a h c e e r h t e h t s a d n u o r g k c a b d n a , d a o r , g n i d l i u b s e g a m i l l a f o e z i s e h T . s e g a m i l e b a l d a o r d n a g n i d l i u b f o R O X e h
t inthisdatase tare1500×1500 in m 1 s i n o i t u l o s e r e h t d n a e z i
s 2/pixe.l
Convolu itonNeura lNetworkandSuppor tVectorMachine
. r o t c a r t x e r u o f o s t r a p l a it n e s s e t s o m e r a e n i h c a m r o t c e v t r o p p u s d n a k r o w t e n l a r u e n n o i t u l o v n o C f o s l i a t e d e h t e v i g e w , e r o f e r e h
T thesetwopartsandthebasicarchitectureweusedint hisarticleas . s w o l l o f N N C f o y r o e h T c is a B d e t c e n n o c y l l u f d n a , s r e y a l g n i l o o p , s r e y a l n o i t u l o v n o c : s t r a p c i s a b e e r h t m o r f N N C t n e s e r p e W . s r e y a l r e y a L l a n o it u l o v n o
n o i t a r e p o n o i t u l o v n o
c usesaconvolution kerne lto convolve with the corresponding region ofthe e h t e t e l p m o c o t l e n r e k n o i t u l o v n o c e h t s e v o m y l s u o u n it n o c n e h t d n a , e u l a v a n i a t b o o t e g a m i
m i e h t t e g l l i w r e tl i f h c a e , n o i t a r e p o n o i t u l o v n o c r e t f A . e g a m i e r it n e e h t f o n o i t u l o v n o
c age of
n o i t c n u f n o i t a v it c a n a o t n i s e r u t a e f e s e h t t u p n i o t d e e n e w , n e h t d n A . s e r u t a e f d e t c a r t x e g n i d n o p s e r r o c
x a m = ) x ( f : n o i s s e r p x e e h t s a h h c i h w , U L e R n o it c n u f e s u e w , e l c i t r a s i h t n I . t u p t u o l a n i f e h t t e g o t
n o c t s a f e r a U L e R f o s e g a t n a v d a e h T . ) x , 0
( vergenceandsimplegradien.t
g n il o o
P .Conventiona lCNN isa continuous convolution operaiton and pooling isan importan t f o s d o h t e m n o m m o c t s o m e h T . e s i c n o c e r o m n o it c a r t x e e r u t a e f e k a m o t s r e t e m a r a p e c u d e r o t p e t s
x a m e r a g n i l o o
p -pooling andmean-pooling .Weadop ttheformeronein thisarticle .Theextracted n
o n l a r e v e s d n a x i r t a m a s a d e t a e r t e r a s e r u t a e
f -overlappingregionsaredivided on thismatrix .We h t n i e t a p i c i t r a p o t d e s u e r a s e u l a v e s o h t d n a n o i g e r h c a e n i s e r u t a e f e h t f o m u m i x a m e h t e t a l u c l a
c e
e s e h t f o t s e g n o r t s e h t y l n o n i a t e r e w t a h t s t n e s e r p e r e u l a v m u m i x a m e h t g n i k a T . g n i n i a r t t n e u q e s b u s
. e p y t s i h t f o s e r u t a e f k a e w r e h t o e v a e l d n a s e r u t a e f
r e y a L d e t c e n n o C y ll u
F .Fullyconnectedl ayersconnectt heiral lnodest ot henodesi nt heprevious a
l yerandmapt hel earnedfeaturest ot hesamplemarkupspace.
M V S f o y r o e h T c is a B
e h t h t i w s e i r o g e t a c o w t o t n i e l p m a s e h t s e d i v i d t a h t e n a l p r e p y h a d n i f o t s i M V S f o e s o p r u p e h T
n a c t I . d n i f o t m i a e w h c i h w e n a l p r e p y h e h t f o t n e i c i f f e o c e h t t n e s e r p e r o t ω e s u e W . l a v r e t n i t s e g r a l
y b d e t n e s e r p e r e
b Eq.1.
x a
m ||𝜔||1 , 𝑠. 𝑡. , 𝑦𝑖(𝜔𝑇𝑥𝑖+ 𝑏) ≥ 1, 𝑖 = 1, … , 𝑛. ( 1)
. 1 e r u g i F s a d e t n e s e r p s i M V S f o m a r g a i d c i t a m e h c s e h t d n A
e r u g i
F 1. SchematicdiagramofSVM.
y f i s s a l c d l u o h s e w , r e i f i s s a l c y r a n i b a s i f l e s t i M V S e c n i
S onet ypeofsamplesi ntot hesameclass o t d e e n e w , s e l p m a s f o s e p y t k e r a e r e h t n e h w , e r o f e r e h T . s s a l c r e h t o n a o t n i s e n o g n i n i a m e r e h t d n a
e r u t c e ti h c r A c is a B
2 e r u g i
F . Basicarchitectureoft hisarticle.
e s a b e h t s w o h s 2 e r u g i
F architecture we use in this article . Our architecture following the y l l u f h t i w d e k c a t s e r a s r e y a l g n i l l o p l a i t a p s d n a s r e y a l l a n o i t u l o v n o c h c i h w n i N N C f o c i t s i r e t c a r a h c
M V S e e r h t o t n i N N C y b d e t c a r t x e s e r u t a e f e h t t u p n i e w n e h T . d e w o l l o f s r e y a l d e t c e n n o
c classifiers .
4 6 * 4 6 a e k a t e W . s r e t e m a r a p e l b a n i a r t g n i n i a t n o c s r e y a l e v i f e r a e r e h
T -sized three-channe lRGB 8
6 7 a d n a t u p n i e h t s a h c t a p y r e g a m i l a i r e
a -dimensiona lvectorastheoutput .Thenwereshapethe 6
1 * 6 1 a o t n i t u p t u
o -sizedt hree-channe lpatchmadeupofbuildings-roads-backgroundchannels .We b
* b h t i w r e y a l l a n o i t u l o v n o c a s i ) c / b * b , a ( C t a h t e m u s s
a -sizedfiltersand theconvolutionstridec , n I . s t i n u a h ti w r e y a l d e t c e n n o c y l l u f a s i ) a ( C F d n a , b e d i r t s h t i w r e y a l g n il o o p x a m a * a n a s i ) b / a ( P
h
t isway,t hearchitecturecanbei nterpretedasC(64 ,16*16/4)-P(2/1)-C(112 ,4*4/1)-C(80 ,3*3/1) -)
6 9 0 4 ( C
F -FC(768).
Methodology
o t S e g a m i l a i r e a t u p n i n a n i s l e x i p w a r m o r f g n i p p a m a n r a e l y l t c e r i d n a c e w , N N C r u o g n i n i a r t y B
e g a m i l e b a l e u r t
a 𝑀� .Andweaimt opredic tamutli-channe llabeli mage𝑀 �fromS .Int hisarticle ,a e s e h t p a m e w d n A . d n u o r g k c a b d n a , s d a o r , s g n i d l i u b g n i d u l c n i s l e n n a h c e e r h t f o s t s i s n o c e g a m i l e b a l
R ( s l e n n a h c B G R o t s l e n n a h c e e r h
t -roads ,G-buildings ,B-background)sot ha teachpixe lont hel abe l 3
a s i e g a m
i -dimensiona lvector .Sinceeachpixe lshouldalwaysbeeitherbackground ,buildingsor n a s i 3 e r u g i F . 1 e b s y a w l a d l u o h s r o t c e v l e x i p a f o s t n e m e l e l l a r e v o m u s e h t , e g a m i l e b a l a n i s d a o r
. e l p m a x e
e
g a m i t u p n
i truel abeli mage 3
e r u g i
F . Exampleofani nputi mageandi tst ruel abeli mage.
h c t a
P -basedFormula iton
a e s u e W ] 1 [ . l a t e h i n M y b d e s o p o r p n e e b e v a h d o h t e m e h t o t r a l i m i s s i d o h t e m g n i l e b a l l e x i p r u O
wS* wS -sized aeria limagerypatch sto obtain awm * wm -sizedtruelabe lpatch𝑚�by training the
e s u d n a N N
C 𝑚�todenotet hepredictedpatch .Wedescribepixell ocatedati i n𝑚�asa3-dimensiona l e
n
o -ho tvector ,𝑚�𝑖 [= 𝑚�𝑖1 ,𝑚�𝑖2 ,𝑚�𝑖3] .In a predicted labe lpatch 𝑚� ,each pixe la t iis also a 3 -r
o t c e v l a n o i s n e m i
i p l l a , n e v i
g xelsin a truelabe lpatch𝑚�𝑖(i=1 ,…𝑤𝑚2) arecondtiionally independen tof each other .
s a d e s s e r p x e e b n a c h c t a p l e b a l e u r t a f o r o i r e t s o p e h t , e r o f e r e h
T Eq.2.
𝑝(𝑚�|𝑠) = ∏𝑤𝑚𝑝(𝑚�𝑖|𝑠). 2
𝑖=1 ( 2)
s a d e b i r c s e d e b n a c n o it c n u f s s o l e h
T Eq.3.
𝐿 = − ∑𝑤𝑚𝑙 𝑝(𝑚�𝑛 𝑖|𝑠). 2
𝑖=1 )(3
a n o g n it a r t n e c n o C . s e h c t a p t u p t u o d n a t u p n i e h t s w o h s 4 e r u g i
F small-regionpatch ,tii ssoabstrac t n o i t a m r o f n i t x e t n o c g n i s u n o i g e r r e d i w a r e d i s n o c e w , e r o f e r e h T . s i ti t a h w e z i n g o c e r t o n n a c e w t a h t
d e s a B . g n i d l i u b a f o t r a p a s w o h s h c t a p e h t t a h t d n i f n a c e w , y a w s i h t n I . s l e b a l t c i d e r p o t s u p l e h o t
o c t x e t n o c n
o nsideration ,thesizeofaninpu tpatchwsisse tlargerthanthesizeofapredictedlabe l
w h c t a
p m .Thist echniquei salsoi mplementedt ohavebetterperformancebyMnihe tal .[14]
4
e r u g i
F . Inpu tandoutpu tpatches.
l e n n a h
C -wsieI nhibtiedSo tfmax[ 8]
, e l c i t r a s i h t n
I wS =64 ,wm =16 .Wereshapea 768-dimensiona lvectorto a16×16×3-sized image
x [ = i x e s u e W . h c t a
p i1 ,xi2 ,xi3] Ttodenotetheithpixe loftheoutpu tpatch .TheSoftmaxisdefined
s a Eq.4.
𝑚�𝑖𝑘 =∑e (𝑥𝑗xe (𝑥pxp𝑖𝑘𝑖)𝑗). (4)
r o t c e v y t i l i b a b o r p l e b a l e h t o t i x t r e v n o c e
W 𝑚�𝑖 = [𝑚�𝑖1, 𝑚�𝑖2, 𝑚�𝑖3]𝑇 asEq.5.
𝑚�𝑖𝐶𝑘𝐼𝑆 =∑e (𝑐𝑗xe (𝑐pxp𝑘𝑥𝑗𝑥𝑖𝑘𝑖)𝑗), 𝑐𝑘 = � 0, 𝑓 1, 𝑡𝑜 ℎ𝑖 𝑘 = 1,𝑒𝑟𝑤𝑖𝑠𝑒. ( 5)
s g n i d li u b f o s l e x i p f o r e b m u n e h t n a h t r e ll a m s h c u m s i s l e x i p d n u o r g k c a b f o r e b m u n e h t , y t i c e h t n I
e r o f e r e h T . s d a o r d n
a ,wese tk=1 ,ck=0t oeilminatet hei nfluenceoft hebackground.
N N
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d l u o h s t c a r t x e o t d e e n e w s e r u t a e f e h T . n o i t a c i f i s s a l c n i e l o r t n a t r o p m i n a s y a l p n o it c a r t x e e r u t a e F
l a n o i ti d a r t g n i s u , M V S r o F . s e s s a l c t n e r e f f i d n e e w t e b h s i u g n i t s i d y l e v it c e f f
e methods to extrac t
e m i t d n a t l u c i f f i d a s i s e r u t a e
f -consuming task .And the trained CNN network can simply and e w , e r o f e r e h T . y l l a c i t n a m e s d e b i r c s e d e b o t e l b a t o n e r a t a h t s e r u t a e f d e c n a v d a e h t t c a r t x e y l t n e i c i f f e
e h t f o e c n a m r o f r e p e h t g n i v o r p m i e g a s i v n
e mode lbycombiningCNNandSVMt ogether.
e h t h t i w s M V S e e r h t h t i w r e y a l t u p t u o s ' N N C e h t e c a l p e r e w , n e h T . l e d o m N N C a n i a r t e w , t s r i F
t i f o t N N C y b d e t c a r t x e s l e b a l d n a s e r u t a e f e h t e s u e w , t a h t r e t f A . d e g n a h c n u g n i n i a m e r l e d o m N N C
n e h W . s M V S e e r h t e s e h
t weuseCNN-SVMt opredictr emotesensingi mages ,wecanuseappropriate .l
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t u o p o r D
, g n i n r a e l p e e d n i g n i n i a r t k r o w t e n f o s s e c o r p e h t n I . g n i t t i f r e v o t n e v e r p y l e v i t c e f f e o t t u o p o r D e s u e W
s ti n u k r o w t e n l a r u e n e h
t are discarded from the network temporarily in acertain probabliity .For i
n i m h c a e , t n e c s e d t n e i d a r g c i t s a h c o t
r e p o o c e h t n o s d n e p e d r e g n o l o n s t h g i e w e h t f o l a w e n e r e h t , y a w s i h t n I . g n i p p o r
d ation ofimplici t
r e h t o r e d n u y l n o e v i t c e f f e g n i e b m o r f s e r u t a e f n i a t r e c e h t g n i t n e v e r p , s n o i t a l e r d e x i f h t i w s e d o n
. s e r u t a e f c i f i c e p s
t n e m e c a l p si D l a it a p S h ti w g n it c i d e r P
n i g i r o e h t e c a l p s i d e W . g n i t c i d e r p n i e s u e w t n e m e c a l p s i d l a i t a p s e h t s w o h s 5 e r u g i
F a linpu taeria l
e s o h t f o s e h c t a p l e b a l d e t c i d e r p e h t , n e h T . e m it h c a e l e x i p e n o h t i w s e m i t n e v e s r o f s e h c t a p y r e g a m i
e d i v i d d n a r e h t e g o t s e h c t a p l e b a l d e t c i d e r p e s o h t e l it e W . t n e m e c a l p s i d e m a s e h t e v a h l l i w s n o i s r e v
e v a n a t e g o t t h g i e y b s e u l a v l e x i p l l
a rage.
5 e r u g i
F . Spatia ldisplacementi nprediction.
s t u p t u o e h t g n i h t o o m s n i e l o r t n a t r o p m i n a y a l p n a c d o h t e m s i h t , s e h c t a p l e b a l d e t c i d e r p e h t r o F
. s s e n e v i t c e f f e s t i s w o h s 6 e r u g i F . e c n a m r o f r e p e h t e v o r p m i y l t n a c i f i n g i s n a c d n a s e i r a d n u o b e h t r e v o
6
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i n M d n a M V
S - CNN-SVMwithCISfourmodelst oshowt heeffecitvenessofourCNN-SVM. i
n i m t p o d a e w , g n i n i a r t g n i r u
D -batchstochasticgradien tdescen tmethodwithmomentum .Inthe g
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l stage,t hehyper-parametersaret hemini-batchsize,t hel earningr ate( LR)η,t heLRr educing t h g i e w 2 L e h t f o t h g i e w a d n a , α m r e t m u t n e m o m e h t f o t h g i e w a , τ y c n e u q e r f g n i c u d e r R L e h t , γ e t a r
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η -batchsizeequalst o128 . i t l u m n g i s e d e W . N N C d e n i a r t e h t m o r f s e r u t a e f d e t c a r t x e h t i w s M V S n i a r t e
W -classificationSVM
s g n i d l i u b , d n u o r g k c a b y f i s s a l c o
t ,androadsandadoptt heone-versus-res tmethod .Thet rainingsets :
s a n w a r d e r a
r o t c e v e h t d n a , t e s e v i t i s o p e h t s a s i d n u o r g k c a b e h t o t g n i d n o p s e r r o c r o t c e v e h t e k a T . 1
; t e s e v i t a g e n e h t s a d a o r e h t d n a s g n i d li u b o t g n i d n o p s e r r o c
r r o c r o t c e v e h t e k a T .
2 espondingtobuildingsasthepositiveset ,and thevectorcorrespondingto ;
t e s e v i t a g e n e h t s a s d a o r d n a d n u o r g k c a b e h t
o t g n i d n o p s e r r o c r o t c e v e h t d n a , t e s e v i t i s o p e h t s a d a o r e h t o t g n i d n o p s e r r o c r o t c e v e h t e k a T . 3
e h t s a s g n i d l i u b d n a d n u o r g k c a b e h
t negativese.t
n o i t c i d e r p e e r h t e h t e p a h s e r e w , n o i t c i d e r p r o F . s M V S e e r h t t e g e w , s t e s g n i n i a r t e e r h t e s e h t g n i s U
i n i m t u p n i e w , t a h t r e t f A . x i r t a m 3 x n n a o t s t l u s e
r -batchlabelsandfeaturesextractedfromtrained h
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i n M h t i w t e
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m building and road ,components of the prediciton resutls h c u s , e g a m i e n o o t n i m e h t g n i v a s d n a , y l e v i t c e p s e r , B G R f o s t n e n o p m o c , R d n a , G , B o t d n o p s e r r o c
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T Weuseprecisionandrecallt oevaluatet heextracitonresults .Precisioni st heraitooft henumber . s e g a m i l e b a l d e t c i d e r p e h t n i e s o h t f o r e b m u n e h t o t s e g a m i l e b a l e u r t n i s l e x i p s d a o r r o s g n i d l i u b f o
s i r a p m o c a s i e r e H . s l e x i p e u r t e h t o t s l e x i p d e t c i d e r p e h t f o o it a r e h t s i l l a c e
R on ofprecision and
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A AverageRecall AveragePrecision AverageRecall
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M -CNN-SVM 0.8987596 0.87341092 0.93471307 0.71739616
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M -CNN-SVMwithCIS 0.89154397 0.89168285 0.84641856 0.84685401
n o i s i c e r p e h t s w o h s 9 e r u g i F d n a 8 e r u g i
F -recal lcurveofMnih-CNNandMnih-CNNwithCIS.
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e c n a m r o f r e p r e tt e b e h t d n A . N N C f o e c n a m r o f r e p e h t e v o r p m i y l t n a c i f i n g i s d n a s e i r a d n u o b e h t r e v o
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T �𝑚�𝑖1,𝑚�𝑖2,𝑚�𝑖3�mappedt o[ 1,1,1]because y l n o e c n i S . e m it e m a s e h t t a ) s d a o r d n a , s g n i d l i u b , d n u o r g k c a b ( s e s s a l c e e r h t o t g n o l e b t o n n a c l e x i p a
e h t f o e s a c e h t r o f , s d a o r d n a s g n i d li u b h t i w s e r u t a e f e m a s e h t e v a h y a m s s a l c d n u o r g k c a b e h t
e w , ] 1 , 1 , 1 [ t l u s e r n o it c i d e r
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r o t c e v h t i w s l e x i
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o and roads) .Since the background has no significan t .
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e h t f o h t o B . n o it a r e d i s n o c r u o o t n i ] 1 , 1 , 0 [ d n a ] 1 , 0 , 1 [ , ] 0 , 1 , 1 [ s t l u s e r n o it c i d e r p e h t o s l a e W
c d n u o r g k c a b a n i a t n o c s t l u s e r o w t t s r i
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t e h t e k a t e w o s , s d a o r f o n o i t c e t e d e h t h t i w e r e f r e t n i o s l a l l i w s s a l c g n i d l i u b e h
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] 0 , 0 , 0 [ , ] 0 , 0 , 1
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t the truth labels to develop a proper conversion d n a , s g n i d li u b , d n u o r g k c a b n e e w t e b s m e l b o r p e c n e r e f r e t n i e h t e v l o s y l e v i t c e f f e n a c h c i h w , y g e t a r t s
n o i s r e v n o c e h t , s d e e n c i f i c e p s r o F . l e d o m e h t f o e c n a m r o f r e p e h t e v o r p m i y lt n a c i f i n g i s d n a s d a o r
a c y g e t a r t
s n help to ge tbetter precision .In thefuture ,we wli limproveour CNN-SVM mode lto o s , s c i t n a m e s e h t o t g n i d r o c c a y g e t a r t s n o i s r e v n o c e h t e g n a h c d n a e r u t c i p e h t f o t x e t n o c e h t y f i t n e d i
d o m r u o d e t n e m e l p m i e w , y l l a n i F . n o i t c i d e r p r e t t e b a e v a h l l i w e w t a h
t elswithanewand flexible d n a s d o h t e m r u o f o s e d o c e h t d n a s t e s a t a d r u o w o h s l l i w e w d n A . r e n i a h C , k r o w e m a r f g n i n r a e l p e e d
t a s t n e m i r e p x
e https://github.com/natrueSwitch/CNN-SVM.
t n e m e g d e l w o n k c A
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U ,theNationa lNatura lScienceFoundationofChina(No . ) 7 2 C R 7 1 0 2 . o N ( s e i t i s r e v i n U l a r t n e C e h t r o f s d n u F h c r a e s e R l a t n e m a d n u F e h t d n a , ) 4 4 0 2 0 7 1
6 .
s e c n e r e f e R
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[ X. L ni Z , . Liu ,J. Zhang ,J. Shen ,Combiningmultiplealgorithmsforroadnetworkt rackingfrom .
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