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MeteorScan Overview

and other

Transient Detection Algorithms

Pete Gural

peter.s.gural@saic.com

Meteor Orbit Determination Workshop #3April 17, 2010

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Algorithmic Development Considerations

Imaging Modalities and Purpose

All sky – Fireball survey and meteorite recovery

Moderate FOV – Meteor flux, mass index, stream characterization

Telescopic – Ablation, orbits, spectroscopy, lunar impacts

Throughput - Real-time, Near-real-time, or Post-collection

Detection - Fast (high SNR) or robust (low SNR) algorithm

False alarms - Tolerance for and mitigation approach

Computing - Processing capacity, storage, interfaces

Analysis - Calibration, Cueing and/or Science exploitation

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Detection Algorithm Choices

Streak Detection

Matched Filter – Hypothesize motion, shift and stack, then threshold

Best Pd, Pfa but large hypothesis count limits the application to meteors

Hough Transform – Threshold pixels, transform to Hough space, find peaks feed MF

Good Pd, Pfa suitable for near real-time with short latency

Orientation Kernel – Convolve spatial kernel, merge detections via temporal propagation

Good Pd, Pfa suitable for near real-time with short latency

Cluster Tracking – Threshold pixels, locate clusters, motion consistency

Moderate Pd, Pfa suitable for real-time tracking needing rapid response

Spatial Change – Threshold pixels and match to spatial signature

Poor Pd, Pfa useful when the transient leaves no temporal response

Background Removal

Clutter Suppression – Use noise statistics to whiten the imagery

Mean or Median – Good for stationary background, lower noise threshold

Difference Frames – Good for slowly drifting background, fast processing

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MeteorScan 3.20 Overview

• Primarily for Meteor Detection in Video

– Limited analysis capability since users wanted to “roll their own”

– Operates at full resolution and near the recorded rate

• Used by the North American Professional Meteor Community

– Univ. of W. Ontario, NASA/MSFC, SETI

– Originally Real-Time on a Mac circa 1997

– Migrated to Non-RT on a PC/Windows system ingesting AVIs

• MeteorScan Capabilities– Masking and FOV Calibration

– Detection via Hough Transform & MLE

– User confirmation review and editing

– Radiant association and statistics

– Software library for detection-only processing in Windows and Linux

MeteorScan Detection Processing

FrameDifferencing

MaxLikelihoodEstimate

HoughPeaks

HoughTransform

PrimaryThresholding

MLE

<MLE>

Detect?

NoiseTracking

Filters(in blue) Tertiary

MLE Space

SecondaryHough Space

PrimaryImage Space

TrackHypothesis

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Streak Detection - Hough TransformMap spatial coordinate exceedance pixels into Hough space

y

x

Traditional HT3 points on a line

Line in Traditional HT(butterfly self-noise)

Pixel pair HTN2 ops

Phase coded disk HTN ops

– Traditional HT – hypothesis all lines that pass through each point

– Pixel Pair HT - two points define line thus one point in Hough space. Localize pairs to reduce ops count.

– Phase Coded Disk HT – convolve PCD kernel around each point to obtain orientation

PCD

MeteorScan SPFN - LFI

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Confirmation Mode Screen Shot

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MTP Detector: Croatian Meteor Network

• Video Compression via “SkyPatrol”

• CONOPS

– Save one RGB bit mapped file for every N seconds of video

– For each pixel, keep the max value in time and associated frame#

– Extending to temporal mean and std dev (excluding max) for flat fielding

• Max Temporal Pixel (MTP) meteor detection software

• Uses the MeteorScan detection modules, Post-processing by CMN

Maximum Pixel Value Frame Number of Max Reconstructed Video

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CAMS at the SETI Institute

• All-sky coverage with high angular resolution

• CONOPS

– 5 DVRs monitors 20 CCD cameras for motion detection at 2 sites

– Records all cameras via FTP compression (Flat-field Temporal Pixel)

– Download only compressed video snippets containing detections

• MeteorScan processed on DVR archive

• Post-processing for triangulation and orbits by SETI

DVR4 channels

ArchivedDetections

viaMeteorScan

DVR4 channels

DVR4 channels

DVR4 channels

DVR4 channels

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MeteorScan for Telescopic Meteors

• Fragmentation studies, Precise radiant positions

• CONOPS / Issues– Very narrow FOV and large optics deep stellar lm without intensifier !

– Meteor trailing losses still limits meteor lm +6.5

– Small FOV lowers # meteors collected

– Orion 80mm f/5 finder scope • 2x Focal reducer 2 degree FOV and stellar lm=+10.5

• MeteorScan has option for long streaks

Scott Degenhardt’s“Mighty Mini”Orion 50 mm

Short Baseline Meteor Triangulation

5 km

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Transient Video Detection Applications

• LFI Detector for the Spanish Fireball Network

• Massive Compact Halo Object Detection

• Lunar Meteoroid Impact Flash Detection

• Meteor Tracking System

• Meteor Simulation for ZHR

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LFI Detector: Spanish Meteor Network

• Large format CCD: 4K x 4K pixels

– All sky coverage with 2.4 arc-minute resolution

– Non-video system: stellar lm = +10, meteor lm = +2

• CONOPS

– Slow read out CCD 1 snapshot every 90 seconds

• Long Frame Integration (LFI) meteor detection

– Differenced frames ( stars + and -, meteors + or - ), Hough Transform PCD

– Post processing orbital reductions analysis by SPFN

- = HT

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Massive Compact Halo Object Detection

• Jupiter sized objects wandering the galaxy

– Stars briefly wink out from occultation

– Find TNOs in the plane of the solar system

• CONOPS

– Collect pairs of dense star field video

– Search for short timescale occultation

– Use pair coincidence to rule out scintillation

• 2 Telescopes with frame rate CCDs

– Observation of an open cluster with good timing

• MachoScan to identify occulted stars

– Space-time coincidence of recorded AVIs

– Post processing analysis by Mount Allison UniversityFew meters

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LunarScan: Lunar Impact Flash Detection

• Boulder Sized Meteoroids Smashing into the Moon !

– Hypervelocity impact creates a momentary flash

– Duration typically a few tens of milliseconds

– One lasted ½ second !

• CONOPS

– Monitor the dark face of the Moon

– 3 days around first and last quarter

– Minimum of two sites >20 km separation

• LunarScan software to locate flashes

– Register, Track mean and standard deviation

– Threshold, Spatial cluster

– Post-collection analysis by NASA/MSFC

Cam

era

Fiel

dof

Vie

w

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AIMIT Meteor Tracking System

• Increase #s of meteors observed in narrow FOV instruments

– Enables spectroscopy and high resolution triangulation/orbits

• CONOPS

– Wide field camera cues steering system for narrow field instrument

• MeteorCue Detection Algorithm

– Threshold, Fast clustering, Centroid, Track, Mirror Commands

– Response time <100 msec (Galvo), <500 msec (Stepper)

– Post-processing Univ of W. Ontario

Monte Carlo meteor influx simulation for video and visual observations/calibration

Converts video counts Spatial flux ZHR

Earth

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Radiant

Particles assumed to have: Initial direction along radiant vector Random start position in cylinder Fixed begin and end heights Fixed magnitude Initial speed V∞ Fixed population index r Mag distribution = [-12,+6.5] Undergone zenith attraction Not decelerated Distance fading loss Atmospheric extinction loss

Specific to CCD vs. Human: Limiting magnitude FOV geometry FOV look direction Resolution Integration time Angular velocity loss Off-axis perception

MeteorSim Processing

Algorithmic Backup Charts

• MeteorCue

• LunarScan

• Streak Detection

– Matched Filter

– Orientation Kernel

– Fast Clustering

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MeteorCue Processing

ClusterDetection

ThresholdEach Frame

Full FrameImagery

30 fps

Mean, Threshold, & SNRTracking Filters

(Updated on a few rows per frame)

TrackerAssociation

Update

<X>

<X> + k1

Even Field

Odd Field

FastCentroid

Repeatevery

33 msec

Row, Col, SNR

Row, Col, SNR

<SNR> + k2 SNR

MirrorCommands

Alpha-BetaTracker30 Hz

2 x 16-bitDigitalSignalsVx, Vy

LunarScan Processing

Sept 16, 2006

Mean andstandarddeviationUpdate

Threshold

Optional register (PCM translation), Warp mean and to current image

Exceedances

Triplet + Doubletcluster

detector

Image Courtesy

NASA/MSFC

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Streak Detection – Matched FilterUses a “Track-before-Detect” approach

– Remove Mean and Estimate 2Remove Mean and Estimate 2ndnd Order Noise Statistics Order Noise Statistics– Apply Covariance Inverse to Remove Clutter (Whitening)Apply Covariance Inverse to Remove Clutter (Whitening)– Hypothesize Multiple Target Velocity Speeds and Hypothesize Multiple Target Velocity Speeds and

DirectionsDirections– Shift Frames and Add for each hypothesisShift Frames and Add for each hypothesis– Convolve with Smear KernelConvolve with Smear Kernel

Mean RemovalCovariance Estimate

Clutter Removal Velocity HypothesisShift & Stack

Multi-Frame Integration

Threshold DetectDecluster / Culling

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Streak Detection – Orientation KernelSmall scale spatial-only convolution

– Convolve 8 orientation kernels across focal planeConvolve 8 orientation kernels across focal plane– Detections are tested for temporal propagationDetections are tested for temporal propagation– Shown are 5x5 binary kernels (MetRec)Shown are 5x5 binary kernels (MetRec)

Can be higher fidelity with width and Can be higher fidelity with width and fractional fillfractional fill

Can use larger dimensions Can use larger dimensions more kernels more kernels Can be formulated as a spatial matched filterCan be formulated as a spatial matched filter

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Streak Detection – Pixel ClusteringFind Groups of Pixels (Limited Spatial Extent, Track in Time)

ThresholdCrossers

Ro

w I

nd

ices

ColumnIndices

Remove Singletons - Fill 32x32 Cells with Threshold Crossers

Find Highest Peak Counts in 2 x 2 Cell Sums

Define CellSize from MaxMeteor Motion

Per Frame

Scale = 16 pixels / degMax = 51 deg / sec

30 frames / secMax 28 pixels / frame

Cell = 32x32 pixels

1 0 1 1

3 1 3 0 1

1 2 1 0 1

1 4 8 1 3

2 5 1 1 0

1 2 1 1 1