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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

Defence R&D Canada • R & D pour la défense Canada

j

MOLNIYA(40000km)(40000km)

GPS(5000km)

LEO(800km)

GEO(36000km)


( )

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


TLED t bDatabase

Extractedobsolete TLE

Update:detectedsatellites

Observation cycle Observationplanning and

tasking

Data reduction(image

processing)

satellites

gp g)

Observationschedule

Astronomicalimage

Observation


Acquisition modes

• SSM: Sidereal stare mode

• TRM: Track rate mode

• TRM sequence: multiple TRM

– Perfect tracking

– With tracking error


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)


Acquisition of a TRM sequence

Case 2:With tracking

Set track rate

error

1Acquisition123

Image summation


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


False alarm rejection


Bad pixel correction

Noise estimation


Star detection

BackgroundOriginal image

Method 1: iterative polynomial fit(but not streak)

Star removal

Streak detection(matched filter)

Alarm extraction


Image - backgroundProfile of an horizontal line

starsbackgroundg+ noise




Noise estimation


Star detection

Original imageI

Method 2: iterative local means estimation and clipping(but not streak)

Star removal


Alarm extraction

Calculation of the local mean

Firstiteration

Noiseestimation

n 8400

8500 original star profile (PSF)

fi t (15 15)

first clipping


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


Background estimation


Noise estimation


Star detection

Method 2: iterative local means estimation and clipping (but not streak)

Star removal


Alarm extraction

Original image First meanFirst clipping Second mean... after five clipping False alarm rejection


Background residue and noise estimation


Noise estimation


Star detection

Original image; acquiredin sidereal stare mode.

Estimated image background(but not streak)

Star removal


Alarm extraction

F l l j i

Residualbackground:


Residue: Residual background + noiseBackground free image Exclusion zones

< n / 5


Residual noise


Noise estimation


Star detection

Profile of the image residue

(but not streak)

Star removal


Alarm extraction

F l l j i

20

40

60Profile of the image residue

2000

2500Local noise distribution


-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


Star detection … but not the streak


Noise estimation


Star detection

- Star detection and removingBackground-free image Detected stars

(but not streak)

Star removal


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

6

512

11

In(7x7)

In(3x3)


8 710 9

Star removal


Noise estimation


Star detection

Zeroing intensity above threshold ...... inappropriate,

(but not streak)

Star removal


Alarm extraction

create artefacts. False alarm rejection

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


Streak detection


Noise estimation


Star detection

Remaining objects 1st convolution

(but not streak)

Star removal


Alarm extraction


Matched filtre

Solution: iterative matched filter

Several false alarms

l


Matched filter for streak detection


Noise estimation


Star detection

Star profile bStreak profilea

(but not streak)

Star removal


Alarm extraction

Convolvedn/2-n/2

C l dn/2-n/2


Convolvedby

1/nRectangle function

1/n

Convolvedby

n/2-n/2n/2-n/2

Almost a rectangle function

2b/n

Triangle functiona

Defence R&D Canada • R & D pour la défense Canadan-n

n-n

Iterative matched filter


Noise estimation


Star detection

Objects(but not streak)

Star removal


Alarm extraction

New Objects


convolution

22x

clippingpp g


Iterative matched filtert l i


Noise estimation


Star detection

Single stars detected and removed:

Matched filter 1st convolution

3rdeconvolution

(but not streak)

Star removal


Alarm extraction

Remaining objects l False alarm rejection

iterations

Detected streak


Alarm extraction


Noise estimation



Star removal


Alarm extraction

A: Streak with remainingstars (boosted contrast)

B: Convolution peaks (after several iterations)

D: Binary masks generatedwith the clipped peaks


56

7

81

23

5

4 9

Noise still present Noise severely attenuatedin convolved image

Segmentation and labelling


Examples of extracted alarms


Noise estimation


Star detection

A pair of stars

(but not streak)

Star removal


Alarm extraction

A pair of stars False alarm rejection

The streak

A false alarm cause by a noise pattern



Noise estimation


Star detection

Alarm rejection parameters(but not streak)

Star removal


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


Moment ratio criteria


Noise estimation


Star detection

pMyypMxx

(but not streak)

Star removal


Alarm extraction

pMxx

pM


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


Length ratio criteria


Noise estimation


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


Alarm extractionRatio between the length of the length of the detection peak and

the length of the expected streak (129 pixels)

3

3,5


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


Normalized ISNR Measurement


Noise estimation


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


Alarm extraction



Valid detection threshold: determined by probability of detection in next slide

Performance evaluation


Noise estimation



Star removal


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


Simulated image with streak and pairs of stars


Noise estimation


Star detection pairs of stars (but not streak)

Star removal


Alarm extraction


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.


Probability of detection without false alarm (in presence of pairs of stars)


Noise estimation


Star detection false alarm (in presence of pairs of stars) (but not streak)

Star removal


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


Detection probability without false alarm


Noise estimation


Star detection false alarm

ISNR = 2.0 ISNR = 0.5 ISNR = 0.2

(but not streak)

Star removal


Alarm extraction


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.


Detection results


Noise estimation


Star detection

Detections and Alarms

(but not streak)

Star removal


Alarm extraction

-----------------------------------------------------------------------image report: 0144 Expected length (pixels): 68.2495Expected orientation (degrees): 51.0725


p ( g )Alarm <ISNR> Center of intensity Probability of FAindex (counts) (Line) (Sample) (percent)-----------------------------------------------------------------------CONFIRMED DETECTIONS:

7 1.398 649.5 567.7 0.00-----------------------------------------------------------------------POSSIBLE DETECTIONS:

6 0.270 858.8 401.0 1.005 0.261 924.9 301.8 1.00

-----------------------------------------------------------------------OTHER REJECTED ALARMS: 10 1.239 10.5 1001.6 1.001 0.437 34.4 7.8 1.0011 0.297 40.1 1014.5 1.003 0.281 986.0 223.4 1.002 0.190 944.1 77.8 1.008 0.167 912.3 963.7 1.004 0.120 928.9 251.5 1.009 0.102 1013.0 975.1 1.00

-----------------------------------------------------------------------


Processing of images acquired in TRMBad pixel correction

Noise estimation


Noise estimationrecycled



recycledStar detection (but not streak)SSM TRMStar streak detection

(matched filter)

Streak feature extractionrecycled

Star removal


Star streak removal

( )

S t llit d t ti S d i(matched filter)

Alarm extraction Alarm extraction

F l l j ti

Satellite detection Star detection

Streak unmerging


False alarm rejection False alarm rejection

Automatic extraction of streak features:length and orientation


Noise estimation


Streak feature extractionlength and orientation

Fourier

Star streak removal

Star streak detection(matched filter)

Satellitedetection

Stardetection

Streakunmerging

transform

Radontransform

Profile at 145degrees

Alarm extraction


g g

145 degreesProfile at 145degrees

transform

degrees

M i

The zeros of the sin(x)/x functionindicate the length ‘l’ of the

Rect(x/l) function Maximumvalue

Rect(x/l) function,i.e. the streak length


Star detection-1


Noise estimation


Streak feature extraction

Matched filter

clipping

Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Original gray shade image

clipping

Filtered gray shade image Convolved streaks

Alarm extraction


g g


Binary mask: original > 2 n Binary mask: filtered image > 2 n Mask : convolved streaks > 1 n

Star detection-2


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Alarm extraction


g gBright star Faint star

Mean value Thresholdset at 1 n

centroid

Red line: streak profileBlue line: convolution peak profile


Star detection-3


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Binary mask: filtered image > 2 n

Bright star removed New binary mask:convolution peak > n

Alarm extraction


g g

Matched filter

Detected bright starsl k


New convolution peaksDetected bright and faint stars

Star unmerging


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Extraction maskExtracted objectsConvolution peaksAlarm extraction


g g

Modeled peakPeak subtractione sub c o

Geometric centerPeak maximum Peak subtraction

Case of merged stars detected

Second peak detected

Example of unmerging


p

Star detection and unmerging


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Alarm extraction


g g


Star removal and satellite detection


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Condition 1: Satellite intensity > noiseDetection threshold

depends on the measured PSF

Alarm extraction


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)


Detection and alarm rejection


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

3- Rejection:Brighter than close

Alarm extraction


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


ysignal pixelisation

Satellite detection


Noise estimation



Star streak removal


Satellitedetection

Stardetection

Streakunmerging

Alarm extraction


g g

DetectionRejected false alarms


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


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;


* 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.


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