33
Empirical Mode Decomposition for Change Detection and Mine Detection Peter Chu Naval Postgraduate School Monterey, California 5/18/2010

Multi-dimensional Ensemble Empirical Mode Decomposition

  • Upload
    others

  • View
    5

  • Download
    0

Embed Size (px)

Citation preview

Page 1: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition for Change Detection and Mine Detection

Peter ChuNaval Postgraduate School

Monterey, California5/18/2010

Page 2: Multi-dimensional Ensemble Empirical Mode Decomposition

Airborne Laser Mine Detection System (ALMDS)

Page 3: Multi-dimensional Ensemble Empirical Mode Decomposition

L3-Klein 5500 Sidescan Sonar

Page 4: Multi-dimensional Ensemble Empirical Mode Decomposition

Image detecting mine

Page 5: Multi-dimensional Ensemble Empirical Mode Decomposition

How can we detect mine from image such as sidescan sonar?

Page 6: Multi-dimensional Ensemble Empirical Mode Decomposition

Efficient Method

Empirical Mode Decomposition

(Huang et al., 1998)

Page 7: Multi-dimensional Ensemble Empirical Mode Decomposition

One Dimensional EMD

Time Series Analysis

Page 8: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition: Methodology : Test Data

Page 9: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition: Methodology : data and m1

Page 10: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition: Methodology : h1 & m2

Page 11: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition: Methodology : h3 & m4

Page 12: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition: Methodology : h4 & m5

Page 13: Multi-dimensional Ensemble Empirical Mode Decomposition

Empirical Mode DecompositionSifting : to get one IMF component

1 1

1 2 2

k 1 k k

k

k i

i 1

x( t ) m ( t ) h ( t ) ,

m ( t ) m ( t ) h ( t ) ,

.....

m ( t ) m ( t ) h ( t ) .

x( t ) m ( t ) h ( t ) .

hi (t) Intrinsic Mode Function (IMF)

mk(t) Trend

Page 14: Multi-dimensional Ensemble Empirical Mode Decomposition

The Stoppage Criteria

The Cauchy type criterion:

when SD is small than a pre-set value, where

T2

k 1 k

t 0

T2

k 1

t 0

h ( t ) h ( t )

SD

h ( t )

Page 15: Multi-dimensional Ensemble Empirical Mode Decomposition

EMD two modes

Page 16: Multi-dimensional Ensemble Empirical Mode Decomposition

Wavelet-based signal separation 6 modes

Page 17: Multi-dimensional Ensemble Empirical Mode Decomposition

Bi-dimensional EMD (BEMD)

Image Data Analysis

Page 18: Multi-dimensional Ensemble Empirical Mode Decomposition

“2D Spline”

Page 19: Multi-dimensional Ensemble Empirical Mode Decomposition

Methods for Spline in BEMD

• Radial Based Function

• Thin Plate Interpretation

• Delaunay Triangulation

• By Slicing

• NURBS : NonUniform Rational B-Spline

Page 20: Multi-dimensional Ensemble Empirical Mode Decomposition

Radial Based Function

3

2

Linear (r) = r

Cubic splines (r) = r

Thin plate splines (r) = r log(r)

Hardy's mu

1 / 22 2

1 / 22 2

ltiquadrics (r) = r c

Inverse multiquadrics (r) = r c

Exponential splines (r) = exp(-cr)

Gaussian splines

2

m

(r) = exp(-cr )

Compactly supported spline (r) = (1-r) + p(r)

Page 21: Multi-dimensional Ensemble Empirical Mode Decomposition

Thin Plate Smoothing

f

2 2 22 2 2

f 2 2

Thin plate smoothing spline calculates the function f(x,y) so that

it minimizes the integral bending norm, I , over the entire image

f f f I + 2 +

x x y y

R

dxdy

This means the solution of a linear system with as many unknown

as there are data points.

Page 22: Multi-dimensional Ensemble Empirical Mode Decomposition

Delaunay Triangulations

• On the basis of Delaunay triangulation we will calculate maximum envelop using the Bernstein-Bezier fitting and interpolation The Bernstein-Bezier calculation includes an optimal fitting around any maximum point and first order smooth connecting along each adjacent edge of Delaunay triangles.

Page 23: Multi-dimensional Ensemble Empirical Mode Decomposition

The basis of Delaunay triangulation

Page 24: Multi-dimensional Ensemble Empirical Mode Decomposition

Examples of BEMD

Page 25: Multi-dimensional Ensemble Empirical Mode Decomposition
Page 26: Multi-dimensional Ensemble Empirical Mode Decomposition

Original image:

Transverse sonogram, left breast palpable mass.Small, well defined anechoic cyst.

http://www.sonoworld.com/Sonoworld/Galleries/ShowCategoryImages.aspx?CategoryID=30#

Page 27: Multi-dimensional Ensemble Empirical Mode Decomposition

IMFs: 1 2 3 4 5 6

Page 28: Multi-dimensional Ensemble Empirical Mode Decomposition

Combination of IMF 1 and IMF 2 gives the obvious uniform region.Left is the contour figure and right is the Image.

Page 29: Multi-dimensional Ensemble Empirical Mode Decomposition

EMD Effective Method for Identifying

Change Detection

Page 30: Multi-dimensional Ensemble Empirical Mode Decomposition
Page 31: Multi-dimensional Ensemble Empirical Mode Decomposition

After Image Coregistration

Baseline Image Repeat Image

Page 32: Multi-dimensional Ensemble Empirical Mode Decomposition

EMD Difference of the Two Images

My Plan

Change Detection

Page 33: Multi-dimensional Ensemble Empirical Mode Decomposition

Proposed Work

• (1) Development of efficient EMD software for analyzing 2D images

• (2) Analysis of NSWC-PC image data (sidescan sonar, laser, radar, …) for software validation and verification

• (3) Implementation of the software into Navy mine detection platforms such as sidescan sonar, ALMDS, …

• (4) Development of simple and efficient method for identifying change detection