SAR Data Pre-processing and Processing · 1/22/2016 · Non-Parametric Classifiers • No assumtion on the statistical distribution of the data • Robust due to ability to describe

SAR Data Pre-processing and

Processing

(Lecture II- Tuesday 22 December 2015)

Training Course on Radar Remote Sensing and Image Processing

21-24 December 2015, Karachi, Pakistan

Organizers: IST & ISNET

Parviz Tarikhi, PhD [email protected]

http://parviztarikhi.wordpress.com

Alborz Space Center, ISA, Iran

Outline Geometric correction and geo-referencing,

Speckle Suppression Techniques,

Filtering: smoothing filters: Mean, Median, Nonadaptive

Filters: Frost, Sigma-Lee, Gamma Map; Edge Enhancement

for linear feature extraction.

Image Classification of SAR Data

Training and Test Sample Selection,

Comparison of Radar Signatures at Feature Spectral Space,

Run the Classification,

Accuracy Assessment of the classification output.

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2

SAR Data Ascquisation

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3

Geometric correction and geo-referencing

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Geometric Correction

Correction of geometric distortions

involved in the original image,

which are caused by sensor

characteristics and geometry,

attitude of platform and topography

of the earth.

Geo-coded Image

Image which is corrected by

matching with geographic

coordinate system. Geo-code is

usually represented by latitude and

longitude.

Geometric correction and geo-referencing

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Geometric correction is undertaken to avoid geometric

distortions from a distorted image, and is achieved by

establishing the relationship between the image coordinate

system and the geographic coordinate system using

calibration data of the sensor, measured data of position

and attitude, ground control points, atmospheric condition

etc.

Definitions

Speckle Filters

Mono-Temporal Speckle Filters

Frequency Domain

Spatial Domain

Adaptive Filters

Multi-Temporal Speckle Filters

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Speckle Suppression Techniques

Pixel‘s backscatterering value

resolution cell

Re

Im

Red vector: the coherent sum of all

the contributions

• Each grey vector corresponds to a scatterer in

the resolution cell.

• Resultant amplitute of the pixel (red vector) is

the coherent sum of all those individual

contributions. 7 5-Jan-16

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(Sart

i 2

011)


Im

Re

resolution cells

What is Speckle?

Re

Im

Re

Im

Constructive combination

Destructive combination

high radiometry

low radiometry

SAR Image

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S

AR

-ED

U, htt

ps:

//sa

redu.d

lr.d

e/

What is Speckle? • Speckle is an inherently salt-pepper noise that degrades the quality of

the SAR images.

• Speckle makes interpretation of features more difficult.

TERRASAR-X Stripmap

Thuringia, Germany

Satelite image of the same area

© GoogleMaps 9 5-Jan-16

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Speckle Reduction

• Speckle in SAR generally causes difficulties for image interpretation,

automatic segmentation etc..

Speckle reduction is made attempting for minumum loss of :

• radiometric information

• spatial resolution

• edge information

• textural information

Ic Measured Intensity

R Intensity value to be estimated

v Speckle

Despeckling techniques aim to estimate R

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Mono-Temporal

Speckle Filters

Multi-Temporal

Spatial Domain Frequency Domain

Non-Adaptive Filters

Adaptive Filters

• Multi-looking

• Lee & Refined Lee

• Kuan

• Gamma Map

• Frost

• Mean

• Median

• Texture Compansation

Multichannel Filter

• 3D Adaptive

Neighbour-hood Filter

• Time-Space Filter


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AR

-ED

U, htt

ps:

//sa

redu.d

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• The reduction of speckle effect (coherent

process) can be achieved by averaging a

sufficient number of independent samples

for each resolution cell (incoherent process)

• Improvement in radiometric resolution

but degradation of spatial resolution

Frequency Domain;

Multi-looking

What do we want to calculate?

• Multi-Look Factor in Range direction (MLR) • Multi-Look Factor in Azimuth direction (MLA)

How to select an appropriate number of looks?

The number of looks is a function of: • pixel spacing in azimuth (AZ) • pixel spacing in slant range (SR) • look angle at scene center (β) • the desired final resolution (x)

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S

AR

-ED

U, htt

ps:

//sa

redu.d

lr.d

e/

Multi-looking

range: 3 azimuth:3 range: 5 azimuth:6 range: 9 azimuth:10

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D

LR


For filtering in spatial domain a

moving kernel is used.

Depending on the filter used, a new value

is assigned to the center pixel.

Spatial Domain

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Mean filter

Median filter

R mean of 9 pixels

R median value of 9 pixels

I Intensity value of a pixel

n number of pixels in the moving window

(size of the kernel)

Mean filter & Median filter

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Spatial Domain;

9 2 8 5 2 2

7 3 7 9 7 7

5 7 5 9 4 4

5 9 1 6 8 7

8 6 7 8 6 8

9 8 5 9 2 5

Mean filter

R=(9+2+8+7+3+7+5+7+5)/9≈6 R=(2+8+5+3+7+9+7+5+9)/9≈6 R=(8+5+2+7+9+7+5+9+4)/9≈6 R=(5+2+2+3+7+7+9+4+4)/9≈5 R=(7+3+7+5+7+5+5+9+1)/9≈5

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R=5 R=7 R=7 R=7 R=5

Median filter

9 2 8 5 2 2

7 3 7 9 7 7

5 7 5 9 4 4

5 9 1 6 8 7

8 6 7 8 6 8

9 8 5 9 2 5

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Mean filter

Mean 3*3 Mean 5*5 Mean 9*9

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Median filter

Median 3*3 Median 5*5 Median 9*9

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Mean filter and Median filter

Mean 9*9 Median 9*9 20 5-Jan-16

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• can be used to supress speckle

noise and in the meantime to

preserve edge information

• exploit local statistics in the

moving window (the statistics

derived from the neighbouring

pixels)

Adaptive filters

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Lee filter

Kuan filter

W Weighting function

Cu noise variation coefficient-

Ci image variation coefficient σ/Im

Ic Central pixel of filter window

Im mean intensity within filter window

σ Standard deviation of intensity

within filter

ENL effective number of looks

Lee filter & Kuan filter

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Adaptive filters;

Lee filter

Lee 3*3 Lee 5*5 Lee 9*9

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Refined Lee filter

Edge threshold:5000 Edge threshold:9000

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Frost filter

M Weighting values

T Euclidian distance between center pixel and the

neighbouring pixels

DampFactor exponential damping factor

Frost filter

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Frost filter

Frost 3*3 Frost 5*5 Frost 9*9

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GAMMA Map filter

Gamma Map filter

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GAMMA Map filter

GAMMA 3*3 GAMMA 5*5 GAMMA 9*9

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Multi-Temporal Speckle filters

• 2-dimensional Mono-temporal

speckle filters operate image by

image, which has the trade-off

between speckle reduction and

spatial resolution or loss of edge

and texture information.

•Multi-temporal filters benefit from the

additional information brought by the

third dimension (time).

• Prior to filtering the images have to be

radiometrically calibrated and

coregistered.

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Multi-Temporal Speckle filters

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What does Classification means?

Parametric Classifiers

• Minimum-Distance-to-Mean

• K-Means

• Maximum Likelihood

Non-Parametric Classifiers

• (K-) Nearest Neighbor

• Decision Trees

• Artificial Neural Networks

• Support Vector Machines

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What does classification means? • [Automatically] categorizing all pixels in an image in certain

(i.e. predefined) classes or themes

• Thematic classification allocates pixels to classes based on

functions of the spectral (or backscatter) properties

Channel 1 2

3 4

5

IMAGE DATA SET

(e.g. Five Digital Numbers per pixel)

CLASSIFICATION

(into a priori defined classes)

MAP

(Thematic representation of classes)

Schematic Classification Workflow 32 5-Jan-16

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(Lil

lesa

nd

et

al.

2008)


Classification

Manual Photo Interpretation

Computer based Interpretation

Supervised Unsupervised

Parametric Non-Parametric Parametric Non-Parametric Supervised Unsupervised

Overview of method

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Parametric Classifiers

• Implying a specific statistical distribution

• Generally gaussian distribution

• Calculating statistical measurement

(e.g. Standard deviation or Covariance)

Differentiation by algorythm

Non-Parametric Classifiers

• No assumtion on the statistical distribution of the data

• Robust due to ability to describe numerous statistical

distributions other than gaussian distribution

SAR data is not usually

gaussian distributed.

For Radar Remote Sensing

non-parametric classifiers are

more appropriate. 34 5-Jan-16

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(Eck

ard

t et

al.

2014)


Unsupervised Classifiers

• No Training stage

• Purely based on the statistical

distribution of the input data

Differentiation by training concept

Supervised classifiers

• Employing manual traing of the data set to distinguish desired classes

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(2) CLASSIFIACTION STAGE Compare each unknown pixel to spectral patterns;

assign to most similar category

(2) OUTPUT STAGE Present results: •Maps • Tables •GIS Data •…

(1) CLASSIFIACTION STAGE Forming clusters of pixels

according to their spectral properties

Channel 1 2

3 4

5

Urban

Forest

Water

…

DN1

DN2

DN3

DN4

DN5

Pixel (3,7)

IMAGE DATA SET

(e.g. Five Digital

Numbers per pixel)

(1) TRAINING STAGE Collect numerical data from training areas on spectral response patterns

of land cover categories

(3) OUTPUT STAGE Present results: •Maps • Tables •GIS Data •…

Differentiation by training concept Unsupervised vs. Supervised

Unsupervised Supervised

Basic steps of classification

(Lil

lesa

nd

et

al.

2008)


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Minimum-Distance-to-Mean Concept: Supervised

Algorithm: Parametric

Pros: Simple, efficient

Cons: Wweak performance for

classes with high variance

Att

rib

ute

2

Attribute 1

Minimum-Distance-to-Mean Classifier

Training pixel

Pixel under investigation

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ard

t et

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K-Means Clustering A

ttri

bu

te 2

Attribute 1

a +

b +

c +

Concept: Unsupervised


Pros: No interaction or a

priori tuning necessary

Cons: Result depends on

initial cluster centers

Empty classes possible

a + b + c + Cluster centers

Idealized data clusters

K-Means Classifier

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ard

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Maximum Likelihood A

ttri

bu

te 2

Attribute 1

Concept: Supervised


Pros: based on multiple

statisitcal parameters

robust

Cons: Complex Processing

Training pixel

Pixel under investigation

Equiprobability contours

Maximum Likelihood Classifier

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ard

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k = 30; 7x , 10x ,13x

k = 5; 4x , 1x

(K-)Nearest Neighbor

Data point under investigation

Ban

d 2

Band 1

Class A

Class B

Class C

k = 1; 1x

Concept: Supervised

Algorithm: Non-Parametric

Pros: Simple implementation

Cons: Slow for many image bands

strong influence of „k“ on result

K-Nearest Neighbor Classifier 40 5-Jan-16

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(Eck

ard

t et

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Decision Trees

Att

rib

ute

2

Attribute 1

Concept: Supervised


Pros: - Simple Structure

- Fast

- Combination of data possible

Cons: - Complex design phase

Vegetation

No Vegetation

Forest

No Forest

Water

No Water

Broad-leaved

Needle-leaved

Agriculture

Settlement

Decision Tree Classifier

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(Eck

ard

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Artificial Neural Networks (ANN)

Input Units Hidden Units Output Units

Image Channels

Thematic Classes

“A neural network consists of a number

of interconnected nodes.

Each node is a simple processing element

that responds to the weighted inputs it

receives from other nodes.

The arrangement of the nodes is referred

to as the network architecture.”

Concept: Hybrid (Un)-Supervised


Pros: - adequate for non-linear

relations

- Robust & error resistent

Cons: - Complex processing

- Model overfitting, overtraining

- Black Boy System

ANN Classifier

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(Atk

inso

n &

Tat

nal

l 1997)


Support Vector Machines (SVM)

Training Pixel Class B

Att

rib

ute

2

Attribute 1 Training Pixel Class A

Which line divides the classes best?

? B)

C) ?

A) ?

Support Vectors

Concept: Supervised


Pros: - Useful for high dimensional data

- White Box Approach

- very flexible

Cons: - long processing time

- needs multiple iterations

- can stay unresolved

SVM Classifier

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(Eck

ard

t et

al.

2014)


To summarize:

Training and Test Sample Selection,

Comparison of Radar Signatures at Feature Spectral Space,

Run the Classification,

Accuracy Assessment of the classification output.

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Post Classification Processing: Classified images require post-

processing to evaluate classification accuracy and to generalize

classes for export to image- maps and vector GIS.

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Accuracy

The closeness of measurement or estimates to the true value.

Accuracy is normally represented by the standard deviation of

errors.

Thank you!

&

any question

Documents

SAR Data Pre-processing and Processing · 1/22/2016 · Non-Parametric Classifiers • No assumtion on the statistical distribution of the data • Robust due to ability to describe