07 sar filtering enhancement

8/9/2019 07 SAR Filtering Enhancement

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Radiometric Enhancement

-Outline-

Filtering

Speckle Reduction- Definition; Why speckle filtering; What is the ide

speckle reduction filter

- Non-adaptive filters (FFT filters)

- Adaptive filters (Frost, Lee, MAP Gamma filters)

Edge Detection- Ratio edge detector filter

- Touzi filter



Radiometric Enhancement (cont’d)

-Outline-

Analysis of Image Texture

Visual Enhancement

Contrast Enhancement

Linear Enhancement

Nonlinear Enhancement

- Histogram, Exponential, Logarithmic,Power Law Stretch



Introduction

This section reviews the methods of enhancing theradiometrics of an image using speckle reduction filters,spatial enhancement filters and visual enhancements.

The understanding of radar “speckle” is key to theunderstanding of SAR and SAR radiometricenhancements.

Often the reduction of speckle is desired to improveclassification and/or for enhancement.

To reduce speckle, adaptive filters (e.g. map gammafilter), should be used rather than non-adaptive filters (e.g.FFT filters) on radar imagery.

Adaptive filters take into account the local properties of theterrain backscatter or the nature of the sensor, whereasnon-adaptive filters do not.



Introduction to Speckle

Image variance or “speckle” is a granular noise that inherentlyexists in SAR imagery (Figure 5.1).

Speckle gives a single look image a grainy, salt and pepperappearance and is the dominating factor in radar imagery.

Speckle noise occupies a wider dynamic range than the scene

content itself. Images processed with a small number of 'looks' will have

distribution intensities which are quite asymmetric due tospeckle noise.

Creating a symmetrical histogram may not be the optimumprocedure. Instead, pixels are set to the extreme limits of thedata intensity distribution (e.g. DN values of 0 and 255 for 8-bit

data).

For a detailed review of speckle, see Raney (1998).



What is Speckle?

Speckle is coherent interference of waves scattered from terrainelements observed in each resolution cell.

An incident radar wave interacts with each element of thesurface and surface cover to generate scattered wavespropagating in all directions.

Those scattered waves that reach the receiving antenna aresummed in direction and phase to make the received signalThe relative phase components contain the differentialpropagation paths.

The SAR focusing operation coherently combines thereceived signals to form the image.

The scattered wave phase addition results in both

constructive and destructive interference of individualscattered returns and randomly modulates the strength ofthe signal in each resolution cell.



Figure 5.1 - Example of Speckle



What is Speckle? (cont’d)

Addition of backscatter from a collection of scatterersproduces random constructive and destructiveinterference, see Figure 5.2.

Constructive interference is an increase from the

mean intensity and produces bright pixels. Destructive interference is a decrease from the

mean intensity and produces dark pixels.

These random fluctuations give rise to speckle.

Reducing these effects enhances radiometric

resolution at the expense of spatial resolution.



Figure 5.2 - Speckle

Constructive Interference

Destructive Interference

Result

Result

Example of Homogenous Target

Constructive interference

Varying degrees of interference(between constructive and destru

Coherent

radar waves



Speckle Suppression

Speckle results from a coherent (phase included) process.

Speckle can be reduced by incoherent (amplitude or power) processes.

Speckle reduction (or smoothing) necessarily reduces the resolution(increases the resolution cell size) of single channel SAR data.

Two basic linear processes:

- Multi-look - divides the signal into minimally overlapped frequencbands, processes each to a reduced resolution image, registersthese, detects and adds the detected images. Examples of multi-look processing are shown in Figure 5.3.

- Averaging - detects the full resolution image, performs localaveraging and resampling processes to create reduced resolutioreduced speckle images.

- For distributed targets both processes are equivalent.



Figure 5.3 - Multi-look Processing

Examples of multi

processing. Note t

image chips A, B,

C all have the sam

resolution, but thaimage chips C and

have comparable

image quality facto

(data from an X-ba

airborne SAR, 197

optically processe

(In Principles &

Applications of

Imaging Radar,

Manual of Remote

Sensing, 1998,

Chapter 2 - Raney

pg. 75)Courtesy R.Shuchman and

E. Kasischke,

ERIM

A 6.1 m x 6.1 m

N = 1

QSAR = 0.027

C 6.1 m x 6.1 m

N = 16

B 6.1 m x 6.1 m

N = 4

QSAR = 0.11

D 1.5 m x 2.13 m

N = 1



Why Speckle Filtering?

The presence of speckle noise must be considered

when selecting analysis methodologies.

Speckle filtering will permit:

better discrimination of scene targets.

easier automatic image segmentation.

the application of the classical enhancement tools

developed for imagery from optical sensors such

as; edge detectors, per-pixel and texturalclassifiers.



The Ideal Speckle Reduction Filter

Reduce speckle with minimum loss of information

In homogeneous areas, the filter should preserve:

radiometric information

edges between different areas

In textured areas, the filter should preserve:

radiometric information

spatial signal variability: textural information



Families of Speckle Reduction Filters

Non-adaptive filters

The parameters of the whole image signal are considered.

Do not take into consideration the local properties of the terrainbackscatter or the nature of the sensor.

Not appropriate for filtering of non-stationary scene signal.

Examples are the FFT filters.

Adaptive filters

Accommodate changes in local properties of the terrainbackscatter.

- The speckle noise is modelled as being stationary

- The target signal is not stationary since the mean backscatterchanges with the type of target

Examples are the Frost, Lee, Map Gamma, local mean and localmedian filters

Figure 5.4 shows examples of adaptive filters.



Figure 5.4 - Gamma vs. Median Filter

Tapajós, Brazil

May 20, 1996 Beam F2

Original Image

Median 5x5

Map Gamma

5x5



Kernel Size

Examples of Mean, Median and Mode filter kernels(or windows) are shown in Figure 5.5.

Filters are a sub-array of X by Y pixels that movesthrough the image.

All three filters shown in Figure 5.5 are square boxfilters, with a kernel size of 3 by 3 pixels

Degree of smoothing is a function of the size of the

kernel.

As filter kernel size increases, smoothing increases.



Figure 5.5 - Filtering Kernel

Source: CCRS

5 7 4

9 8 65 5 8

MEAN

5 7 49 8 65 5 8

MEDIAN

5 7 4

9 8 65 5 8

MODE

5+7+4+9+8+6+5+5+8= 57

57÷ 9 =

MEAN = 6

4,5,5,5,6,7,8,8,9

MEDIAN = 6

4

555

6 MODE = 5

788

9

3 x 35 x 5

7 x 7



Figure 5.6 - Median and Mean Filters

Tapajós, Brazil


Original Image

Median 7x7

Mean 7x7



Adaptive Filtering

Adaptive filters (e.g. Map Gamma) reduce speckle

while preserving the edges (sharp contrast

variation).

Adaptive filters modify the image based on statistics

extracted from the local environment of each pixel.

Larger kernel size (e.g. 11x11) result in an important

increased smoothing effect on the resulting image

(Figure 5.7).



Figure 5.7 - Gamma Filter

Tapajós, Brazil


Original Image

Map Gamma7x7

Map Gamma11x11



Most Well-known Filters: The Frost Filter

Principle

The unspeckled pixel value is estimated using asubwindow of the processing window.

The size of the subwindow varies as a function

of target local heterogeneity measured withcoefficient of variation:

– the larger the coefficient of variation, thenarrower the processing subwindow

The Enhanced Frost Filter (Lopes, Touzi and Nezri,IEEE, 1990) minimizes the loss of radiometric andtextural information (Figure 5.8).



Figure 5.8 - Examples of Filters



Most Well-known Filters : The Lee Filter

Principle

The unspeckled pixel value is a weighted sum ofthe observed (central) pixel value and the meanvalue.

The weighting coefficient is a function of localtarget heterogeneity measured with the coefficienof variation.

The Enhanced Lee Filter (Lopes, Touzi and Nezri,IEEE, 1990) minimizes the loss of radiometric andtextural information (Figure 5.8).

The Enhanced Lee and Enhanced Frost Filtersperform similarly.



Most Well-known Filters :

The MAP Gamma Filter

Background

The Frost and Lee filters are based on models

which do not use the statistical properties of the

underlying scene.

In a joint study with CESR (Toulouse, France),

CCRS participated in the development of the

MAP Gamma Filter (Lopes, Touzi, Nezri and

Low, IJRS, 1993).



Most well known Filters :

The MAP Gamma Filter (cont’d) Principle

The filter is based on the assumption that the(unspeckled) intensity of the underlying scene isgamma distributed.

The filter minimizes the loss of texture information betterthan the Frost and Lee filters within gamma distributedscenes.

It is suitable for a wide range of gamma distributed scenes,such as forested areas, agriculture areas, and oceans.

The filter preserves the observed pixel value for non-

gamma distributed scenes.

See Figure 5.9 for the filter example.



Figure 5.9 - Map Gamma Filter

Tapajós, Brazil


Original Image Map Gamma11x11



Figure 5.11 - Effects of Filtering

Filter Size & Type vs % Change in SD

Filter Size & Type

% C

h a n g e i n S t a n d a r d D e v i a t i o n

Raw Median 7x7 Lee 7x7 Frost 7x7

Frost 3x3

Lee 5x5

Lee 3x3Median 3x3

Median 5x5 Frost 5x5

Source: CCRS



Figure 5.12 - Effects of Filtering

Source: CCRS, Brown et al, 1993

Effects of Filtering on Sample Wheat Field Statistics, ERS-1 SAR

Mean Standard

Deviation

% Change

in Mean

% Change

in SD

Mean/SD

Raw

Median 3x3

Median 5.5

Median 7x7

Lee 3x3

Lee 5x5

Lee 7x7

Frost 3x3

Frost 5x5

Frost 7x7



Edge Detection in SAR Images

Application : Segmentation of the image into separateentities, classification

Types of Edge Detection Filters:

Directional, Gradient, Laplacian, Sobel, Prewitt,Ratio Edge Detector

Warnings The classical edge detectors (e.g. Gradient, Sobel)

developed for imagery from optical sensors are notsuitable for SAR images.

Because of the multiplicative nature of speckle,they detect more false edges within brighter areas.

Imagery must first be filtered (Gamma) prior tousing the classical edge detectors.



Edge Detection in SAR Images (cont’d)

Potential alternatives

The ratio edge detector (R. Touzi et al., IEEE

TGRS, 1988) is suitable for SAR images and does

not require pre-filtering.

Performance of the ratio edge detector is better

since information is lost during pre-filtering for the

classical edge detectors.



Ratio Edge Detector Filter

(Touzi, et. al., 1998)

Original SAR image

Gradient image (5x5)

Ratio Edge Detector (5x5)

- For the gradient detector, the probability that a pixel of a homogeneo

area is assigned to edges (Pfa) is dependent on the mean power due

multiplicative nature of the noise.

- The operator detects more false edges in brighter areas.

- The ratio edge detector is the ratio of the average of pixel values of t

nonoverlapping neighborhoods on opposite sides of the point.

- The Pfa does not depend on the mean power

- The performance of the ratio edge detector is a function of the size o

neighborhoods, the number of looks and the ratio of the mean powers



The Touzi multi-resolution speckle Filter

All the most well known adaptive filters were developed under theassumption that the signal is stationary within the moving processingwindow of a fixed size (i.e. its mean and variance do not vary within tobservation time).

The filters are not effective primarily when applied to fine structursuch as roads and trails which are generally smoothed out by thefilters.

A new multi-resolution filter the Touzi Filter (Figures 5.13 and 5.14)was developed at CCRS (a part of PCI software 2002 version).

The size and the shape of the filter processing window are adapteto signal nonstationarity.

The Touzi multi-resolution ratio edge detector is used for betterfiltering of contours and edges (Touzi et al., IEEE TGRS 1998)

This permits more efficient speckle reduction and a betterpreservation of the scene spatial variations (texture, edges, point

targets).

Source: R. Touzi, CEOS workshop 1999



Figure 5.13 - Touzi Filter

Tapajós, Brazil


Original Image Touzi filter



Figure 5.14 - Touzi Filter

Original ImageTouzi filter

15X15

Lee filter 7X7

RADARSAT-1 image

Fine Mode



Introduction to Texture

Texture is the spatial variation of tones in an image.

Image texture may be qualitatively described ashaving properties like fineness, coarseness,

smoothness, granulation, randomness, lineation,mottled, irregular, hummocky (Figure 5.15).

In a SAR image, texture has two components: (1)spatial variability in the scattering properties of thescene and (2) speckle.



Figure 5.15 - Image Texture

Corn Field Forest

300 m

Spatially Uniform TargetFine Texture

Spatially Non-Uniform TargetCoarse Texture

300 m



Texture Analysis (cont’d)

Texture

Textural features statistics can be extracted using a

grey level Co-Occurrence Matrix (GLCM). User specific neighborhood parameters.

Examples of features from GLCM:

- Homogeneity - Mean

- Contrast - Standard deviation

- Dissimilarity - Entropy

- Angular second moment - Correlation

Speckle suppression techniques may not preserve all

scene texture details.



Contrast Stretch

A contrast stretch enhances visual interpretation(Figure 5.16).

Matches data’s dynamic range to dynamic range of displa

Involves the construction of a look-up table (LUT).

LUT is a graphical model of the mathematical function

selected.



Figure 5.16 - Contrast Stretch

Original image Linear Stretch

Rosario, Argentina



Linear Stretch

Effective upper and lower cutoff values are

established.

Upper and lower histogram values are set to

maximum & minimum limits respectively.

May use full or piecewise stretch.

Balance of the data are stretched linearly tofill the expanded display range.

See Figure 5.17.



Figure 5.17 - Linear Stretch



Nonlinear Enhancements

Distort the image radiometry.

Useful only for visual interpretation.

quantitative radiometric information can be lost.

spatial information is preserved.

results may not be replicable from scene to scene.



Histogram Stretch

Input display range may not be fully utilized.

Output display range makes full use of thedynamic range.

Enhances the contrast where frequency of

occurrence is greatest.

Options include:

- Inverse frequency- Frequency equalization

- Gaussian normalization- Histogram matching



Inverse Frequency (or Infrequency)

Produce an image in which the bright pixels

represent those grey levels in the originalimage which were infrequent.

LUT is derived from an inverted (upside

down) histogram of the input image datavalues.

Useful for highlighting rare or small featuresin an image (lineaments or edges).

Figure 5.18 is an example of infrequency

enhancement.



Frequency Equalization

Redistribute pixel values so that there are

approximately the same number of pixels for eachdata value available.

More for visual display than for image analysis.

Figure 5.19 is an example of Frequency

Equalization.



Figure 5.19 - Frequency Equalization



Exponential Stretch

High-range brightness is enhanced and highhistogram skew can be corrected.

Details in the higher part of the dynamic rangeare revealed.

An example of an algorithm for this stretch is e x .



Logarithmic Stretch

Low-range brightness is enhanced and histogram

skew may be corrected.

Skewness is common and may invalidate some imag

analysis algorithms which assume a normal data

distribution.

Also known as root Enhancement.

Root ( log N ).

Tends to lend an overall brightening to the resultant

image (see figure 5.20).



P L St t h



Power Law Stretch

Changes the image brightness, S , as a power law:

S new = S n

n > 1 enhances strong returns at the expense ofweak returns.

n < 1 ( n ) enhances weak returns at the expenseof strong returns.

The special casen

= 2 converts a magnitudeimage to a power image.

Alters the probability distribution (histogram) of the

data and may invalidate processes based onGaussian assumptions.

QUANTITATIVE QUALITATIVE

“TYPICAL” SAR IMAGE PROCESSING METHODOLOGY



CONVERSION FROM DN TO:

° or °

(dB)

° or °

(power)

INTERFEROMETRY

- DEM generation

- Coherence image- Surface change detection

FILTER

(speckle reduction)

- Adaptive filters

- Non adaptive filters

STEREOSCOPY

- DEM generation

- Planimetric feature

extraction

CHANGE DETECTION

(e.g. ratio, difference)

CALCULATION OF

TARGET SIGNATURES

CONVERT POWER

VALUES TO dB

MODELLING- Theoretical backscatter

Geophysical parameters

TEXTURE ANALYSIS

(input for classification)FILTER

(speckle reduction)

- Adaptive filters

- Non adaptive filters

ENHANCEMENT(for visual interpretation)

- High pass filters

- Low pass filters

- FFT filters

- Contrast stretch

GEOMETRIC CORRECTION

- Ortho-rectification using DEM

- Slant / ground range conversion

- Polynomial transformation

DATA FUSION

- RGB-IHS Colour Space

- Principal Component

Analysis

- Vector Overlay

CLASSIFICATION

S i dACCURACY

AUTOMATED FEATUREEXTRACTION

- image thresholding

- edge detection, lineaments

- directional filters (Sobel, etc.,)

OTHER DATA

- multitemporal SAR

- optical RS

- geophysical

- Thematic polygons

or vectors (GIS)

- etc.

QQUALITATIVE

INFORMATION

EXTRACTION

- Valued-added

information map

AMPLITUDE

Digital Number

(DN)

AMPLITUDE + PHASE

Single Look Complex

(DNI + DNQ)

STEREOSCOPY

- terrain interpretation

07 sar filtering enhancement

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