igarss11benedek.pdf

ISAR Image Sequence based Automatic TargetRecognition by using a Multi-frame Marked Point

Process Model

Csaba Benedek1 Marco Martorella2

1Distributed Events Analysis Research GroupComputer and Automation Research Institute, Hungary

2University of Pisa, Department of Information Engineering

Work partially funded by the APIS Project of EDA

IGARSS 2011, Vancouver, Canada

Content

1 Introduction

2 Multiframe Marked Point Process ModelModel elements and configuration energyOptimization

3 Experiments

4 Future steps and conclusions

Benedek & Martorella (SZTAKI, CNIT) Target Extraction in ISAR Image Sequences 28 July 2011 2 / 19

Introduction

Content

1 Introduction


3 Experiments



Introduction

Introduction

Extracting ship scattering centers in airborne Inverse SyntheticAperture Radar (ISAR) image sequences

Framework: “Array Passive ISAR Adaptive Processing” (APIS)Project of EDA

ISAR images in Automatic Target Recognition (ATR) systemsapplicable where other imaging techniques (e.g. SAR) failpost processing step after detection & imagingframes have different quality parameters (e.g. image focus)

Goals:

Measuring relevant features for target identification and behaviouranalysis


Introduction

Proposed approach

Robust multi-frame technique, integrating the noisy imageinformation with prior constraints of target shape persistency andsmooth motion.

Multiframe Marked Point Process model

Input:ISAR image sequence of the detected target

Output:center line segment parameters of the target in each frame

length and orientation

positions of permanent characteristic feature points


Multiframe Marked Point Process Model

Content

1 Introduction


3 Experiments



Multiframe Marked Point Process Model Model elements and configuration energy

Configuration model: notations

Observation: n-frame-long ISARimage sequence

S: joint pixel lattice of the ISARframes, s ∈ S: a single pixelBt : binarized input image observedat time frame t ∈ {1, 2, . . . , n}Bt (s) ∈ {0, 1}: value of pixel s in Bt

ut ∈ H: a target candidate in frame tGoal: extract a sequence of objects:

ω = {u1, u2, . . . , un} ∈ Hn



Target modeling in a single ISAR frame

Parameters describing a target u:c(u) = [x(u), y(u)] center pixel, l(u) length and θ(u) orientation

Misalignment problem

periodicity of ISAR images both in horizontal and vertical directionstarget may break into two/four piecesusing a duplicated mosaic image



FmMPP energy function

Object sequence or configuration:

ω = {u1,u2, . . . ,un}

Configuration energy:

ΦD (ω) =

n∑

t=1

AD (ut)+γ ·

n−1∑

t=1

I (ut ,ut+1)

AD (ut): D-data dependent unary object potentialI (ut , ut+1) prior interaction potential function between objects ofconsecutive frames

Maximum Likelihood (ML) configuration estimate:

ω̂ = argminω∈Hn

ΦD (ω)



Data term

Unary potentials:evaluation of proposed shipcandidates in independent frames

Calculation:

AD (ut) = Q

1

Area{Rut ∪ Tut}

∑

s∈Rut

Bt (s)+∑

s∈Tut

(1 − Bt (s))

Q(ζ) : R → [−1, 1]: a non-linear monotonously decreasing function



Interaction potentials

Key role: enforcing prior geometrical constraints.

persistent frame rate → small object displacements between twoconsecutive frames

Feature: length and angle difference (center is not relevant)

I (ut ,ut+1) = δθ · |θ (ut)− θ (ut+1)|+ δl · |l (ut)− l (ut+1) |

Penalized configuration ×

Favored configuration√


Multiframe Marked Point Process Model Optimization

Optimization: iterative stochastic algorithms

The algorithm1 Start with a frame-by-frame initialization process

Hough transform based line estimation in each binarized frame Bt ,t = 1 . . . n

2 Iterate object perturbation and acceptance steps till convergence isobtained in the extracted object sequence

Object perturbation: for each t we propose an object u∗ which is therandom perturbation of ut−1 OR ut OR ut+1

Acceptance: we accept or reject a move replacing ut width u∗

Important properties:Acceptance: inverse approach considering simultaneously dataand prior featuresStochastic process both for object perturbation and acceptance(unlike in conventional hypothesis generation-acceptancetechniques)Simulated annealing framework to ensure convergence


Multiframe Marked Point Process Model Optimization

Target identificationPermanent scatterer extraction and counting

Permanent scattererresponses: characteristictarget features

high false/missing alarm ratein the individual frames(>50%)histograming technique forextracting the permanentscatters


Experiments

Content

1 Introduction


3 Experiments



Experiments

Experiments - qualitative results

Center alignment and target line extraction results

Top: input sequence. Center: frame-by-frame detection. Bottom: detection by the

proposed FmMPP model


Experiments

Experiments - quantitative results

Test sequences:four airborne ISAR image sequences (each has 15-40 frames)different ship targets.

Error measure:

E({ut}, {ugtt }) =

n∑

t=1

(|x(ut)− x(ugt

t )|+ |y(ut)− y(ugtt )|+

+ |l(ut)− l(ugtt )|+ |θ(ut)− θ(ugt

t )|)

Sequence Frames Init Err. FmMPP Err.

Ship 1 13 52.0 7.5Ship 2 13 67.1 37.8Ship 3 13 17.2 12.8Ship 4 54 43.7 12.6


Future steps and conclusions

Content

1 Introduction


3 Experiments




Generalization for various objects

Identifying Airplanes in ISAR sequencesCross shaped modelShadowed wing

Result by the two step process



Conclusions

Detecting and featuring ship/airplane targets in ISAR imagesequences through energy minimization

Proposed Multi-frame Marked Point Process schema

advantages versus a frame-by-frame direct detection technique

Towards target classification

permanent scatterer detection algorithm based on histograming


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