Marine Targets Detecction inction in Pol-SAR...
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Marine Targets Detec Marine Targets Detec Peng Chen, Ji Peng Chen, Ji State Key Laboratory of Satellite State Key Laboratory of Satellite Second Institute of Oceanography, State Ocean E-mail: chenpe E mail: chenpe ABSTRACT：In this poster, we present a new method of marine target detection in Pol-SAR data. One band SAR ABSTRACT：In this poster, we present a new method of marine target detection in Pol SAR data. One band SAR l ih B fl d i h h id l b f Ai h bi i kl algorithm. But some false detection may happen, as the sidelobe of antenna, Azimuth ambiguity, strong speckle decomposition and false color composite, the sidelobe of antenna and Azimuth ambiguity could be deleted. So, the lt f Rd t 2 SAR i t t i di t d Th dt ti lt d ith At ti I result of Radarsat-2 SAR image test indicates a good accuracy . The detection results are compared with Automatic In 1 INTRODUCTION 1. INTRODUCTION Monitoring of marine targets is very important for managing sea transportation and safeguarding the state’s marine rights and interests Now the normal methods of marine targets monitoring include state s marine rights and interests. Now, the normal methods of marine targets monitoring include airborne remote sensing and spaceborne remote sensing. Airborne remote sensing has rapid reaction rate, precise identification and strong adaptability but it could be affected easily by weather conditions and precise identification and strong adaptability, but, it could be affected easily by weather conditions and high cost. By contrast, spaceborne remote sensing has wide monitoring range and low cost, but limited by image resolution and orbit position. Microwave remote sensing can work in all-day and all-weather and image resolution and orbit position. Microwave remote sensing can work in all day and all weather and b ih hi h l i S h i A Rd (SAR) i f h j observe open sea state with high space resolution, Synthetic Aperture Radar (SAR) is one of the major payloads. Pol-SAR data can provide polarization information. So, in this study, polarization information i d t dt t i t t d dlt fl l is used to detect marine targets and delete false alarms. 2 DATA Th l i i d i hi d i f Rd 2 fi d h hi i i h ff h 2. DATA The polarization data in this study is from Radarsat-2 fine mode, the geographic site is the offshore area of the Dongying city, Shandong province of China. More details about the data shows in Table 1. The dt i f ll l i ti ith 8 t l ti d i t iti i 38°12'N d 118°53'E data is full polarization with 8 meters resolution, and image center position is 38°12'N and 118°53'E. Figure 1 is the SAR image 1 and Figure 2 is SAR image 2 . Table 1. Data information of this study d S i (UTC) B Pl R l i L /L C data Start time (UTC) Beam Polar Resolution Lat/Long Center 1 2011 05 16 10:08:31 FQ25 HH+VH+VV 8m 38°12'N/118°53'E 1 2011-05-16 10:08:31 FQ25 HH+VH+VV 8m 38 12'N/118 53'E 2 2014-11-24 05:47:15 FQ25 HH+VH+VV 8m 30°01'N/121°52'E 2 2014 11 24 05:47:15 FQ25 HH+VH+VV 8m 30 01 N/121 52 E Figure 1. SAR image 1 in the study Figure 2. SAR image 2 in the study 3. METHOD AND RESULT 3. METHOD AND RESULT 3 1 Et /lh/ i t d iti 3.1 Entropy /alpha/ anisotropy decomposition Entropy /alpha/ anisotropy decomposition is one of the incoherent decomposition. It performs an eigen- decomposition of the coherency matrix of a full-polarimetric Single Look Complex data set. When the medium decomposition of the coherency matrix of a full polarimetric Single Look Complex data set. When the medium satisfies the conditions of reciprocit (SHV SVH) Matri [S] co ld be presented as follo ith ector form: satisfies the conditions of reciprocity (SHV=SVH), Matrix [S] could be presented as follow with vector form: (1) 1 (1) [ ] T S S S S S 2 1 k − + = [ ] HV VV HH VV HH S S S S S 2 2 k + = 2 H/α/A decomposition is based on Matrix T3: 1 0 0 ⎤ ⎡ λ T kk T ∗ (2) T 1 U 0 0 0 0 U T ⎥ ⎤ ⎢ ⎡ λ λ T kk T ∗ = (2) T 2 U 0 0 U T ⎥ ⎥ ⎥ ⎢ ⎢ ⎢ = λ 0 ≥ ≥ ≥ λ λ λ 3 0 0 ⎥ ⎦ ⎢ ⎣ λ 0 3 2 1 ≥ ≥ ≥ λ λ λ 3 ⎦ ⎣ 3 ∑ − = 3 log P P H Entropy : Alpha : P P α α α α P + + = ∑ = = 3 1 log i i i P P H Entropy : Alpha : 3 3 2 2 1 1 P P α α α α P + + = = 3 1 i ∑ = 3 / P λ λ A i t 3 2 A λ λ − ∑ = 1 / j j j i P λ λ Anisotropy : 3 2 A λ λ + = = 1 j 3 2 λ λ + Figure 3 is false colour composite image of H/α/A decomposition, the white points show high confidence level that points are from the true ship target Some purple points red points and green confidence level that points are from the true ship target .Some purple points , red points and green points are false target points. Fi 3 Fl l it i f H/α/A decomposition Figure 3. False colour composite image of H/α/A decomposition ID:10412 ID:10412 ction in Pol SAR Data ction in Pol-SAR Data ingsong Yang ingsong Yang e Ocean Environment Dynamics e Ocean Environment Dynamics nic Administration, Hangzhou 310012, China; [email protected] [email protected] R image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR) R image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR) i d i h i l b d SAR i Pl SAR i if i f Af noise and so on in the single band SAR image. Pol-SAR image can get more information of targets. After e method presented include three steps, decomposion, false color composite and supervised classification. The d tif Si t (AIS) dt th f i ht dt ti i b 95% d fl dt ti ti i bl 5% ndentify Sistem (AIS) data, the accuracy of right detection is above 95% and false detection ratio is below 5%. Fi 4 i th i t t dt ti fl h t i f ll l SAR dt Fi tl d th H/ /A Fig.4 is the marine target detection flow chart using full pol-SAR data. Firstly, do the H/α/A decomposition , then get three bands images. These bands are corresponding R,G,B colors. Then, do the false colour composite After training the neural net can separate the real target and false In the end false colour composite. After training , the neural net can separate the real target and false. In the end , the NN work do the classification of the whole image. Fig.5 shows some examples of NN training. The samples are collected from the same marine points in the SAR image samples are collected from the same marine points in the SAR image. Figure 4. Flow chart of Figure 5. Training sample of NN Marine target detection 3.2 Target Detection NN l ifi ti l t t l l d f d f d l t k l ifi ti t hi 3.2 Target Detection NN classification use neural net to apply a layered feed-forward neural network classification technique. The Neural Net technique uses standard backpropagation for supervised learning. We select the number of hidden layers to use and you can choose between a logistic or hyperbolic activation function of hidden layers to use and you can choose between a logistic or hyperbolic activation function. Learning occurs by adjusting the weights in the node to minimize the difference between the output node activation and the output The error is backpropagated through the network and weight adjustment is activation and the output. The error is backpropagated through the network and weight adjustment is made using a recursive method. Figure 6. A Layered feed-forward neural network Figure 7. RGB false colour image of Entropy Figure 8. result of NN classification /Alpha/ anisotropy decomposition T bl 2 Result of hi detection in the whole image Table 2. Result of ship detection in the whole image Fl Method Detection AIS False Miss Detect rate False Method Detection AIS False Miss Detect rate detect rate NN 129 127 8 6 95 2％ 62％ NN 129 127 8 6 95.2％ 6.2％ 4 DISCUSSION AND CONCLUSSIONS 4. DISCUSSION AND CONCLUSSIONS In this paper, we discuss marine targets detection in Pol-SAR data using NN . The polarization decomposition can get more information than gray level from single band SAR data Considered the decomposition can get more information than gray level from single band SAR data. Considered the value of Entropy /alpha/ anisotropy , we find if the three band projects to the RGB image, the result of classification is better than the result of classification from Entropy /alpha/ anisotropy bands directly classification is better than the result of classification from Entropy /alpha/ anisotropy bands directly . In the study, the sample of NN is an important factor . Some cases indicates the shape of ship are not integrated, so, an added procedure of CFAR detection should be carry out to enhance the performance integrated, so, an added procedure of CFAR detection should be carry out to enhance the performance f h hd of the method.
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Transcript of Marine Targets Detecction inction in Pol-SAR...
Marine Targets DetecMarine Targets Detec
Peng Chen, JiPeng Chen, JiState Key Laboratory of SatelliteState Key Laboratory of Satellite
Second Institute of Oceanography, State OceanE-mail: chenpeE mail: chenpe
ABSTRACT：In this poster, we present a new method of marine target detection in Pol-SAR data. One band SARABSTRACT：In this poster, we present a new method of marine target detection in Pol SAR data. One band SAR
l i h B f l d i h h id l b f A i h bi i klalgorithm. But some false detection may happen, as the sidelobe of antenna, Azimuth ambiguity, strong speckle
decomposition and false color composite, the sidelobe of antenna and Azimuth ambiguity could be deleted. So, thep p , g y ,
lt f R d t 2 SAR i t t i di t d Th d t ti lt d ith A t ti Iresult of Radarsat-2 SAR image test indicates a good accuracy. The detection results are compared with Automatic In
1 INTRODUCTION1. INTRODUCTION
Monitoring of marine targets is very important for managing sea transportation and safeguarding the
state’s marine rights and interests Now the normal methods of marine targets monitoring includestate s marine rights and interests. Now, the normal methods of marine targets monitoring include
airborne remote sensing and spaceborne remote sensing. Airborne remote sensing has rapid reaction rate,
precise identification and strong adaptability but it could be affected easily by weather conditions andprecise identification and strong adaptability, but, it could be affected easily by weather conditions and
high cost. By contrast, spaceborne remote sensing has wide monitoring range and low cost, but limited by
image resolution and orbit position. Microwave remote sensing can work in all-day and all-weather andimage resolution and orbit position. Microwave remote sensing can work in all day and all weather and
b i h hi h l i S h i A R d (SAR) i f h jobserve open sea state with high space resolution, Synthetic Aperture Radar (SAR) is one of the major
payloads. Pol-SAR data can provide polarization information. So, in this study, polarization informationp y p p , y, p
i d t d t t i t t d d l t f l lis used to detect marine targets and delete false alarms.
2 DATA
Th l i i d i hi d i f R d 2 fi d h hi i i h ff h2. DATA
The polarization data in this study is from Radarsat-2 fine mode, the geographic site is the offshore area
of the Dongying city, Shandong province of China. More details about the data shows in Table 1. Thegy g y, g p
d t i f ll l i ti ith 8 t l ti d i t iti i 38°12'N d 118°53'Edata is full polarization with 8 meters resolution, and image center position is 38°12'N and 118°53'E.
Figure 1 is the SAR image 1 and Figure 2 is SAR image 2 .g g g g
Table 1. Data information of this study
d S i (UTC) B P l R l i L /L Cdata Start time (UTC) Beam Polar Resolution Lat/Long Center
1 2011 05 16 10:08:31 FQ25 HH+VH+VV 8m 38°12'N/118°53'E1 2011-05-16 10:08:31 FQ25 HH+VH+VV 8m 38 12'N/118 53'E
2 2014-11-24 05:47:15 FQ25 HH+VH+VV 8m 30°01'N/121°52'E2 2014 11 24 05:47:15 FQ25 HH+VH+VV 8m 30 01 N/121 52 E
Figure 1. SAR image 1 in the study Figure 2. SAR image 2 in the studyg g y
3. METHOD AND RESULT3. METHOD AND RESULT
3 1 E t / l h / i t d iti3.1 Entropy /alpha/ anisotropy decomposition
Entropy /alpha/ anisotropy decomposition is one of the incoherent decomposition. It performs an eigen-
decomposition of the coherency matrix of a full-polarimetric Single Look Complex data set. When the mediumdecomposition of the coherency matrix of a full polarimetric Single Look Complex data set. When the medium
satisfies the conditions of reciprocit (SHV SVH) Matri [S] co ld be presented as follo ith ector form:satisfies the conditions of reciprocity (SHV=SVH), Matrix [S] could be presented as follow with vector form:
(1)1 (1)[ ]TSSSSS 21k −+= [ ]HVVVHHVVHH SSSSS 22
k +=2
H/α/A decomposition is based on Matrix T3:
1 00 ⎤⎡λTkkT ∗ (2)T
1
U0000
UT ⎥⎤
⎢⎡
λλ
TkkT ∗= (2)T2 U00UT
⎥⎥⎥
⎢⎢⎢= λ
0≥≥≥ λλλ300 ⎥⎦⎢⎣ λ 0321 ≥≥≥ λλλ3⎦⎣
3
∑−=3
log PPHEntropy : Alpha : PP αααα P++=∑=
= 31
log ii
i PPHEntropy : Alpha : 332211 PP αααα P++==
31i
∑=3
/P λλ A i t 32A λλ −∑=1
/j
jjiP λλ Anisotropy : 32Aλλ +
==1j
32 λλ +
Figure 3 is false colour composite image of H/α/A decomposition, the white points show high
confidence level that points are from the true ship target Some purple points red points and greenconfidence level that points are from the true ship target .Some purple points , red points and green
points are false target points.
Fi 3 F l l it i f H/α/A decompositionFigure 3. False colour composite image of H/α/A decomposition
ID:10412ID:10412
ction in Pol SAR Dataction in Pol-SAR Data
Jingsong YangJingsong Yange Ocean Environment Dynamicse Ocean Environment Dynamicsnic Administration, Hangzhou 310012, China; [email protected]@sio.org.cn
R image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR)R image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR)
i d i h i l b d SAR i P l SAR i i f i f Afnoise and so on in the single band SAR image. Pol-SAR image can get more information of targets. After
e method presented include three steps, decomposion, false color composite and supervised classification. Thep p , p , p p
d tif Si t (AIS) d t th f i ht d t ti i b 95% d f l d t ti ti i b l 5%ndentify Sistem (AIS) data, the accuracy of right detection is above 95% and false detection ratio is below 5%.
Fi 4 i th i t t d t ti fl h t i f ll l SAR d t Fi tl d th H/ /AFig.4 is the marine target detection flow chart using full pol-SAR data. Firstly, do the H/α/A
decomposition , then get three bands images. These bands are corresponding R,G,B colors. Then, do thep , g g p g , , ,
false colour composite After training the neural net can separate the real target and false In the endfalse colour composite. After training , the neural net can separate the real target and false. In the end ,
the NN work do the classification of the whole image. Fig.5 shows some examples of NN training. The
samples are collected from the same marine points in the SAR imagesamples are collected from the same marine points in the SAR image.
Figure 4. Flow chart of Figure 5. Training sample of NNMarine target detection
3.2 Target Detection
NN l ifi ti l t t l l d f d f d l t k l ifi ti t h i
3.2 Target Detection
NN classification use neural net to apply a layered feed-forward neural network classification technique.
The Neural Net technique uses standard backpropagation for supervised learning. We select the numberq p p g p g
of hidden layers to use and you can choose between a logistic or hyperbolic activation functionof hidden layers to use and you can choose between a logistic or hyperbolic activation function.
Learning occurs by adjusting the weights in the node to minimize the difference between the output node
activation and the output The error is backpropagated through the network and weight adjustment isactivation and the output. The error is backpropagated through the network and weight adjustment is
made using a recursive method.
Figure 6. A Layered feed-forward neural network g y
Figure 7. RGB false colour image of Entropy Figure 8. result of NN classification/Alpha/ anisotropy decomposition
g
T bl 2 Result of hi detection in the whole imageTable 2. Result of ship detection in the whole image
F lMethod Detection AIS False Miss Detect rate
False Method Detection AIS False Miss Detect rate
detect rate
NN 129 127 8 6 95 2％ 6 2％NN 129 127 8 6 95.2％ 6.2％
4 DISCUSSION AND CONCLUSSIONS4. DISCUSSION AND CONCLUSSIONS
In this paper, we discuss marine targets detection in Pol-SAR data using NN . The polarization
decomposition can get more information than gray level from single band SAR data Considered thedecomposition can get more information than gray level from single band SAR data. Considered the
value of Entropy /alpha/ anisotropy , we find if the three band projects to the RGB image, the result of
classification is better than the result of classification from Entropy /alpha/ anisotropy bands directlyclassification is better than the result of classification from Entropy /alpha/ anisotropy bands directly.
In the study, the sample of NN is an important factor. Some cases indicates the shape of ship are not
integrated, so, an added procedure of CFAR detection should be carry out to enhance the performanceintegrated, so, an added procedure of CFAR detection should be carry out to enhance the performance
f h h dof the method.
