Satellite detection algorithms Satellite detection algorithms Satellite detection algorithms Satellite detection algorithms developed for developed for SofS applications SofS applications Warning: This presentation contains several animations that cannot Martin P. Lévesque RDDC Valcartier P d be executed with the PDF format. Some slide may look like confused because of this Presented to: NATO SET147- Microsatellites and Surveillance of Space July 15th 2010 confused because of this. Defence Research and Development Canada Recherche et développement pour la défense Canada Canada
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Satellite detection algorithmsSatellite detection algorithmsSatellite detection algorithmsSatellite detection algorithmsdeveloped for developed for
SofS applicationsSofS applicationsWarning:
This presentation contains several animations that cannot
Martin P. LévesqueRDDC Valcartier
P d
be executed with the PDF format.
Some slide may look like confused because of this Presented to:
NATO SET147- Microsatellites and Surveillance of SpaceJuly 15th 2010
confused because of this.
Defence Research andDevelopment Canada
Recherche et développementpour la défense Canada Canada
PLAN
- SofS context- Acquisition modesAcquisition modes- Processing in SSM- Processing in TRM
-ProcessingIt ti b k d l l ith- Iterative background removal algorithm
- Star detection- Streak detection- Streak detection- Iterative matched filter- Alarm extraction and false alarm rejection
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j
MOLNIYA(40000km)(40000km)
GPS(5000km)
LEO(800km)
GEO(36000km)
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( )
Search Area vs Delay of Observation
(TLE) Prediction accuracy after a too long period:
Position estimation accuracy
after a too long period:... lost object
after a certain period of time:... requires a new acquisition
pointingaccuracy
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TLED t bDatabase
Extractedobsolete TLE
Update:detectedsatellites
Observation cycle Observationplanning and
tasking
Data reduction(image
processing)
satellites
gp g)
Observationschedule
Astronomicalimage
Observation
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Acquisition modes
• SSM: Sidereal stare mode
• TRM: Track rate mode
• TRM sequence: multiple TRM
– Perfect tracking
– With tracking error
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Acquisition in SSM : (Sidereal Stare Mode)
Pointing
Sidereal motion
Sidereal trackingSidereal tracking
Expected satellite
Acquisition
DownloadDownload
Data analysis
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Acquisition in TRM : (Track rate Mode)
Sidereal tracking
Set track rate
Expected satellite
Acquisition
Download
Data analysis
Star detectionStar detection
Satellite detection
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Acquisition of a TRM sequence
Case 1:Perfect tracking
Set track rate
Perfect tracking
1Acquisition123
Image summation
VetoVe o(logical and)
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Acquisition of a TRM sequence
Case 2:With tracking
Set track rate
error
1Acquisition123
Image summation
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Processing of images acquired in SSMBad pixel correction
Noise estimation
Background estimation and removal
Star detection (but not streak)
Star removal
Streak detection(matched filter)(matched filter)
Alarm extraction
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False alarm rejection
Background estimation and removal
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
BackgroundOriginal image
Method 1: iterative polynomial fit(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
False alarm rejection
Image - backgroundProfile of an horizontal line
starsbackgroundg+ noise
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Background estimation and removal
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Original imageI
Method 2: iterative local means estimation and clipping(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
Calculation of the local mean
Firstiteration
Noiseestimation
n 8400
8500 original star profile (PSF)
fi t (15 15)
first clipping
False alarm rejection
Image clipping
Iterations2 to 5
n : noise standard deviation Estimated background
(Smoothed image)
<Bi>
n
8200
8300
8400 first mean (15x15)
second mean (15x15)after first clip
third mean (15x15)
second clipping
clipping
Clipped imageIbi
8000
8100
( )
fourth mean (15x15)fifth mean (35x35)
375 380 385 390 395 400 405 410
7800
7900
final background = minimum fourth and fifth means
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Background estimation
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Method 2: iterative local means estimation and clipping (but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
Original image First meanFirst clipping Second mean... after five clipping False alarm rejection
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Background residue and noise estimation
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Original image; acquiredin sidereal stare mode.
Estimated image background(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
F l l j i
Residualbackground:
False alarm rejection
Residue: Residual background + noiseBackground free image Exclusion zones
< n / 5
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Residual noise
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Profile of the image residue
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
F l l j i
20
40
60Profile of the image residue
2000
2500Local noise distribution
False alarm rejection
-20
0
20
coun
t
500
1000
1500
coun
t
0 100 200 300 400 500 600 700 800 900 1000-60
-40
pixel
-80 -60 -40 -20 0 20 40 60 800
500
gray level
Gaussian distributionp
Residual background < n / 5
Gaussian distribution
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Star detection … but not the streak
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
- Star detection and removingBackground-free image Detected stars
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
F l l j iFalse alarm rejection
S t d d bl t filt f Remaining objects
1 2 3 4
In(7x7)
out
Segmented double gate filter for star detection ... but not streak
Central disk set to zeroZone of subtracted profileUnaffected faint star
A B C
MedianMeanStandard deviation
S l i h fil
0 2 4 6 8 10 12024681012
Solution: measure the star profiles
... and subtract it
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Streak detection
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Remaining objects 1st convolution
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
False alarm rejection
Matched filtre
Solution: iterative matched filter
Several false alarms
l
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Matched filter for streak detection
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Star profile bStreak profilea
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
Convolvedn/2-n/2
C l dn/2-n/2
False alarm rejection
Convolvedby
1/nRectangle function
1/n
Convolvedby
n/2-n/2n/2-n/2
Almost a rectangle function
2b/n
Triangle functiona
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n-n
Iterative matched filter
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Objects(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
New Objects
False alarm rejection
convolution
22x
clippingpp g
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Iterative matched filtert l i
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Single stars detected and removed:
Matched filter 1st convolution
3rdeconvolution
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
Remaining objects l False alarm rejection
iterations
Detected streak
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Alarm extraction
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection (but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
A: Streak with remainingstars (boosted contrast)
B: Convolution peaks (after several iterations)
D: Binary masks generatedwith the clipped peaks
False alarm rejection
56
7
81
23
5
4 9
Noise still present Noise severely attenuatedin convolved image
Segmentation and labelling
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Examples of extracted alarms
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
A pair of stars
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
A pair of stars False alarm rejection
The streak
A false alarm cause by a noise pattern
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Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Alarm rejection parameters(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
Moment ratio >10False alarm rejection
Length ratio > 1
<ISNR> (estimation of the local SNR)<ISNR> (estimation of the local SNR)> 0.5 for long streak> 1.0 for short streak
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Moment ratio criteria
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
pMyypMxx
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
pMxx
pM
False alarm rejection
Myy
pMyy / pMxx >> 100 for a streak (typically) pMyy / pMxx 1 for a starpMyy / pMxx < 10 for a pair of starsp yy p f p f
Valid alarm: pMyy / pMxx >> 10
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Length ratio criteria
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Ratio between the length of the length of the detection peak and
Length ratio = length of extraction mask (or convolution peak) > 1length of expected streak
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extractionRatio between the length of the length of the detection peak and
the length of the expected streak (129 pixels)
3
3,5
False alarm rejection
1 5
2
2,5
engt
h ra
tio
L
Mask length Streak length
0,5
1
1,5Le
Lm
sL
00 5 10 15 20 25 30 35
ISNR
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Normalized ISNR Measurement
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection
Extraction mask(from the convolution
peaks)
Extracted object Standard selection mask (from the expected streak characteristics)
Selected pixels (for intensity calculation)
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
False alarm rejection
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Valid detection threshold: determined by probability of detection in next slide
Performance evaluation
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection (but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction- The matched filter has very good performances- Consequence: Detection thresholds are set to lower value False alarm rejectionConsequence: Detection thresholds are set to lower value
and new false alarms are generated. - False alarm rejection performs very well, so:
what are the ultimate threshold detection limits?
• Calibrated synthetic image for detection testing
streak + PSF + photon noise
+ sensor noise
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Simulated image with streak and pairs of stars
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection pairs of stars (but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
False alarm rejection
Pairs of stars represent th t bthe worst case becausethey are not eliminatedby star detection and removal process.
Two stars with the streakTwo stars with the streakalignment are like a short streak.
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Probability of detection without false alarm (in presence of pairs of stars)
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection false alarm (in presence of pairs of stars) (but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
0 7
0,8
0,9
1 False alarm rejection
0 4
0,5
0,6
0,7
babi
lity
of d
etec
tion
50 pixels78 pixels129 pixels
Streak length
0 1
0,2
0,3
0,4
Prob
0
0,1
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
ISNR
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Detection probability without false alarm
Bad pixel correction
Noise estimation
Background estimation and removal
Star detection false alarm
ISNR = 2.0 ISNR = 0.5 ISNR = 0.2
(but not streak)
Star removal
Streak detection(matched filter)
Alarm extraction
False alarm rejection
100% 100% 50%
Raw probability of detection:The algorithms always detect the streak with ISNR > 0.1, but with false alarms
b bili f d iNet probability of detection:With ISNR > 0.5, always 100% of detection completely without false alarm.
p ( g )Alarm <ISNR> Center of intensity Probability of FAindex (counts) (Line) (Sample) (percent)-----------------------------------------------------------------------CONFIRMED DETECTIONS:
g gmeasured PSFBackground-free image - Stars (i.e. the filtered streaks) = image difference
Insertedtest pattern
for sensitivityevaluation
Condition 2:and
Satellite brighter than the overlapping streakCondition 2: Satellite brighter than the overlapping streak(with enough contrast)
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Detection and alarm rejection
Bad pixel correction
Noise estimation
Background estimation and removal
Streak feature extraction
Star streak removal
Star streak detection(matched filter)
Satellitedetection
Stardetection
Streakunmerging
3- Rejection:Brighter than close
Alarm extraction
False alarm rejection
g g
PSF area
gstreak? (again)- connect to closest object
Closest streakmask2- Rejection:
- Object-to-object comparison: Brighter than mean brightness
Alarm does not overlap close streak mask
of overlapping streak?
Streak in the1- Alarm:
Single-pixel alarm Can be caused by
Streak in thePSF range
- Pixel-to-pixel comparison: Brighter than aligned pixel
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ysignal pixelisation
Satellite detection
Bad pixel correction
Noise estimation
Background estimation and removal
Streak feature extraction
Star streak removal
Star streak detection(matched filter)
Satellitedetection
Stardetection
Streakunmerging
Alarm extraction
False alarm rejection
g g
DetectionRejected false alarms
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Sensitivity of SSM vs TRM acquisition
SSM: PSF=3
MODE Detection threshold
Required objecti t it
SSM PSF 3
SSM: PSF=3,Streak = 130 pixels
210 nintensity
TRM, PSF<1 9 n 9 n
TRM, PSF=3 3 n 45 nSSM: PSF=3,
Streak = 50 pixels 160 nSSM, 50 pixels n 160 n
SSM, 130 pixels n/2 210 n
TRM: PSF=3(3x3) local average
45 n
TRM: PSF<1Single pixel
9 n3 n /2
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n n n/2
Detection algorithms for TRM image sequence
Is in development, it includes:- Star detection and astrometry done for each imagesy g
- Provides knowledge of real pointing (drift estimation)- Image registration
- Uses satellite predicted position and- track rate correction (drift correction) which uses the real sensor
pointing (astrometry) - Summation of registered images
- More sensitive detection (better SNR) with the summed image.- Differentiation of registered images
- Reject star streaks and cosmic ray hits.- Detection in individual frame:
- cued by the global detection,- uses a lower detection threshold;
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* false alarm elsewhere are ignored.
Conclusions
- Detection algorithms developed for both SSM and TRM.- Star and satellite (point or streak) detection is f ll t tifully automatic.
- Iterative background estimation and removal more reliable and accurate than the classical dark frame method.a d accu ate t a t e c ass ca da a e et od.
- Iterative matched filter very efficient for streak detection.- Sensitivity limited by noise level:y y
- streak (very long) > 1 n (> 0.5 n),- single point object (in TRM) > 9 n,- adaptable to the PSF.
- Detection for TRM sequence in development.- TRM acquisition more sensitive than SSM.