multitemp 2005 – biloxi, mississippi, usa, may 16-18, 2005 remote sensing laboratory dept. of...

MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005

Remote Sensing LaboratoryDept. of Information and Communication Technology

University of TrentoVia Sommarive, 15, 38050 Trento, Italy

Francesca BOVOLO and Lorenzo BRUZZONE

E-mail: [email protected] Web pages at: http://www.ing.unitn.it/~bruzzone/RSLab

A Wavelet-Based Change-Detection Technique for Multitemporal SAR Images

2MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005

Outline

Introduction and background.

Aim of the work.

Proposed wavelet-based change-detection approach:

Multiscale decomposition;

Adaptive scale-selection;

Automatic multiscale fusion.

Experimental results.

Conclusions and future developments.


Introduction

Unsupervised change-detection in multitemporal remote-sensing images plays an important role in several application domains (the ground truth on the analyzed problem is not available in many real applications).

Unlike optical multispectral images, Synthetic Aperture Radar (SAR) data are less used in unsupervised change-detection problems. This is due to:

the complexity of SAR data pre-processing;

the presence of multiplicative speckle noise that renders difficult the separation between changed and unchanged classes.

However, the use of SAR images for change detection is particularly attractive from the operational view point as they are not affected by sun light and atmospheric conditions.


Aim of the Work

Develop a novel wavelet-based multiscale approach to unsupervised

change detection in multitemporal SAR images (suitable for single-

channel single-polarization multitemporal SAR data);

Adaptively exploiting information at different scales in order to obtain

change-detection maps that shows:

high accuracy in both homogeneous and border regions;

high geometric fidelity.


Proposed Approach: Block Scheme

Scale-DrivenFusion

X1

t2 SAR image

t1 SAR image

Adaptive ScaleIdentification

ImageComparison

(log-ratio)

“Log-ratio” Image

MultiscaleDecomposition

Change-detection map

Ω ={ωc , ωu }0LRXnLRX

1-NLRX

X2

XLR M


Image Comparison

Difference operator: the distribution depends on both the relative change and the reference intensity level in the original images. Thus changes are not detected in the same way in high and low intensity regions.

Ratio operator: reduces the multiplicative distortion affects common to the two considered images due to speckle noise. The distribution depends only on the relative changes between images.

jjL

D

L

jjLj

jLD

L

D LX

LXL

LXP

21

2111

0! )1( !

! )1(

22121 exp

!1

1),(

Equivalent numberof look of the SAR data

Intensity of SARimages at t1and t2

Usually log-ratio operator is used instead of the ratio as the log-ratio image has a more symmetrical statistical distribution and transforms the residual multiplicative noise model into an additive noise model.

L

R

LR

L

RX

X

L

LXP

212

1

21

221

!1

!12),(


Multiscale decomposition (1)

Objective: compute a multiscale sequence of log-ratio images characterized by different trade-offs between SNR and geometrical-detail content.

l(.)

h(.)

h(.)

l(.)

h(.)

l(.)

Column wiseRow wise

1

0

1

0

1

1

0

1

0

1

1

0

1

0

1

1

0

1

0

1

)2,2()(

)2,2()(

)2,2()(

)2,2()(

D

p

D

q

LLnLR

nHHLR

D

p

D

q

LLnLR

nHLLR

D

p

D

q

LLnLR

nLHLR

D

p

D

q

LLnLR

nLLLR

qjpiXqhphji,X

qjpiXqlphji,X

qjpiXqhplji,X

qjpiXqlplji,X

nLLLRX

Proposed approach: apply iteratively the two-dimensional wavelet transform to the log-ratio image following the Mallat pyramidal algorithm.


Multiscale decomposition (2)

The desired multiresolution sequence

is computed applying inverse wavelet

transform at each resolution level

independently: LL1 LH1 HL1 HH1

Level 1

LL2 LH2 HL2 HH2

Level 2

LLN-1 LHN-1 HLN-1 HHN-1

Level N-1

to all wavelet coefficient after thresholding detail sub-bands;

only to the approximation sub-bands (neglecting detail sub-bands).

LRLR XX 0

(.)l̂ (.)l̂

(.)l̂ (.)l̂ (.)l̂

1LRX2

LRX1NLRX

(.)l̂


Adaptive Scale Identification

2 2

nnL

nLn CV

ji,μji,σji,LCV

Definition: a resolution level is reliable for a given pixel if the pixel does not belong to a border area at that level.

The set of n reliable scales for the generic pixel (i,j), is made up of all the sequential resolution levels that satisfy the following condition:

Homogeneous regionsSij=N-1

Border regionsSij= 0

“Intermediate” regionsLevel 0 < Sij < N-1

Sij is the optimal scale: the lowest resolution level that satisfies the definition of reliable scale for a given pixel.

CV: is the coefficient of variation (normalized standard deviation) computed over the whole image;LCV: is the local coefficient of variation computed over a moving window;


Scale-Driven FusionThree different scale-driven fusion strategies have been considered for generating the change-detection map:

Optimal scale selection (OSS);Fusion at decision level (FDL);Fusion at feature level (FFL).

Optimal scale selection:

nc,k ,ωji,Mωji,M kk withijS 1NS ijand

Pixel label at optimalresolution level Sij

Fusion at decision level:

jiVjiMk

k

kk ,maxarg,

# of times the pixel (i,j) is

assigned to class k over the set of its reliable scales

Pixel label in the finalchange-detection map


Fusion at feature level:

Compute a new set of N images by averaging all possible sequential combinations of reliable scales:

n

h

hLR

nMS

n 01

1XX , with 1,...,1,0 Nn

For each pixel compute the final label as:

ijij

ijij

SSMS

SSMS

TT

ji, ifω

ji, ifωji,M

c

n

X

X

,

Scale-dependent threshold value

Scale-Driven Fusion


Data Set Description

Study area: a forest area in the central Canada (Saskatchewan).

Multitemporal data set: a portion of 350×350 pixels two images acquired by the SAR sensor of ERS-1 the 1st July and the 14th October 1995.

Objective: identify forest fires that affected the considered area between the two acquisition dates.

July 1995 October 1995 Reference Map


Multiresolution Decompositon

Level 4 Level 5 Level 6

Level 1 Level 2 Level 3Level 0 (Log-ratio image)


Experimental Results

Proposed approach

Strategy False Alarms Missed Alarms Overall Errors

OSS 3227 4678 7905

FDL 2634 3777 6411

FFL 2242 3379 5621

Classical algorithms

Strategy False Alarms Missed Alarms Overall Errors

Wavelet de-noising 2769 4243 7012

Lee de-noising 2875 3965 6840


Experimental Results

Classical approach(de-noising with the Lee filter)

Proposed approach(FFL strategy)

Change-detection mapsReference map


Conclusions

A novel adaptive wavelet-based technique for change-detection in multitemporal SAR images has been proposed.

Two novel methodological contributions characterize the proposed method compared with traditional algorithms:

automatic and adaptive selection of the reliable scales to be used in change detection for each pixel;

scale-driven fusion strategies;

The presented technique shows both high sensitivity to geometrical details and a high robustness to noisy speckle components in homogeneous areas;

Experimental results obtained on real multitemporal SAR data confirm the effectiveness of the proposed approach and in particular of the FFL strategy.


Future Developments

Explore different strategies for performing the multiresolution decomposition step in order to proper modeling the change information at different resolution levels (e.g., stationary wavelet transform).

Integrate automatic thresholding procedures in the scale-dependent fusion procedure.

Extend the use of the proposed approach to change detection in very high resolution SAR images and multiband and fully polarimetric SAR data.

multitemp 2005 – biloxi, mississippi, usa, may 16-18, 2005 remote sensing laboratory dept. of...

Documents

ratio images

unsupervised change

use of sar images

changedetection maps

multiscale approach

ratio operator

considered images

original images