segmentation of polarimetric sar data using spectral graph partitioning: utilizing multiple cues...

Segmentation of Polarimetric SAR Data Using

Spectral Graph Partitioning: Utilizing Multiple Cues

Dept. of Electrical and Computer Engineering

University of British Columbia

Vancouver, Canada

Kaan Ersahin*, Ian Cumming and Rabab K. Ward

Motivation

Using Ideas from HVS

Spectral Graph Partitioning (SGP)

Utilizing patch-based similarity in SGP

Utilizing contour information in SGP

Proposed Scheme

Results

Summary

Future Work

OUTLINE

2Segmentation of Polarimetric SAR Data Using Spectral Graph Partitioning: Utilizing Multiple Cues

Motivation

Manual segmentation of SAR data is a common practice Human experts are often good at visual interpretation

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Operational use of polarimetric spaceborne systems means: Daily acquisitions more data to analyze

Wider spectrum of users with limited or no expertise in SAR Polarimetry

Analysis typically involves: Segmentation

e.g., drawing boundaries between agricultural fields, water - ice separation, etc.

Automated segmentation task is very challenging Edge detection followed by linking or region merging methods often do not perform well

Human vision system (HVS) can perform this task easily Identify lines, contours, patterns and regions and make decisions based on global information

Automated analysis procedures are needed To develop better decision making tools that require less analyst (human) interaction

Segmentation of Polarimetric SAR Data Using Spectral Graph Partitioning: Utilizing Multiple Cues

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Local view

Convair-580, C-band, color composite

Global view

© CSA 2004

Importance of using global view


Motivation – Developing a better method

A number of useful analysis techniques have been developed

ML classifier based on Wishart distribution (Lee et. al )

Eigenvalue decomposition H / A / α-angle (Cloude - Pottier)

Target decomposition based on physical models (Freeman - Durden)

… their combinations and variants

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These are based on polarimetric attributes of pixels (or averages in a neighborhood)

Not able to capture the information that human observer can pick up

Visual aspect of image data can be used to enhance automated segmentation results

Study how humans handle this task

Use the ideas that have led to the state-of-the-art technique in computer vision


Using Ideas from HVS

In computer vision problems (e.g., segmentation, object detection) The ultimate goal To reach the performance level of an human expert

61 Gestalt: a configuration or pattern of elements so unified as a whole that its properties cannot be derived

from a simple summation of its parts.

Similarity(e.g., brightness, color, shape)

X X X X O X X X X X X X X O X XX X X X X X O XX X X X X X X O

Proximity(geometric)

X X X X X X

X X X X X X

X X X X X X

Continuity

In computer vision, a promising technique that can utilize these ideas has emerged:

Spectral Graph Partitioning

What does an image mean for humans? More than the collection of pixels, represents a meaningful organization of objects or patterns

In late 1930s, Gestalt Psychologists 1 studied this phenomenon: perceptual organization Several cues (i.e., factors that contribute to this process) were reported:

Closure

Spectral Graph Partitioning (SGP)

A pair-wise grouping technique: an alternative to central grouping

No assumption on the statistical distribution of the data (e.g., Gaussian)

Avoids the restriction that all the points must be similar to a prototype (i.e., class mean)

Enables combination of multiple cues (e.g., different types of features and data sets)

Offers flexibility in the definition of affinity functions (i.e., measure of similarity)

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G = { V , E } is an undirected graph

V nodes (data points or pixels)

E edges (connections between node pairs)

( i , j ) weights (similarity between node i and node j )

W similarity matrix ; its entries are the weights: ( i , j )

WG


Spectral Graph Partitioning

Shi and Malik (2000) showed that solving the eigenvalue problem for the Normalized

Graph Laplacian: provides a reasonable solution.

Yu and Shi (2003) showed that eigenvectors completely characterize all optimal solutions

Space of global optima can be navigated via orthogonal transforms.

Iteratively solve for a discrete solution that is closest to the continuous global optimum using an alternating optimization procedure

Their method is called Multiclass Spectral Clustering (MSC).

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To divide the graph into two partitions, intuitively:

similarity between the resulting partitions or

cost of removing all the connections between the candidate partitions (i.e., cut) should be minimized

A better way: Minimize the Normalized Cut


Utilizing Patch-based Similarity in SGP

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We have used SGP for classification based on patch-

based similarity (IGARSS 2006)

Spectral Clustering algorithm is modified to account for the unique properties of SAR data

Instead of pixel intensities, the histograms calculated within an edge-aligned window mask are used as attributes.

Similarity is measured using the 2 – distance

Form an affinity matrix to account for spatial proximity

Patch-based similarity cues from multiple channels and proximity are combined in an overall affinity matrix (W)


SpeckleReduction

Form affinity matrix (W)

PolSAR Data

Multi-looking

Spectral Graph

Partitioning

Utilizing Contour Information in SGP

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In IGARSS 2007 we used SGP for segmentation based on contour information.

The motivation was:

Region-based techniques perform either:

Sequential merging of segments based on an appropriate measure (e.g., likelihood ratio test)

Optimization of a global objective function

Drawback: contour information – a powerful cue for HVS – is not utilized.

Contour-based techniques often start with edge detection, followed by a linking process.

Drawback: Only local information is used; decisions on segment boundaries are made prematurely

Leung and Malik addressed this issue by collecting contour information locally (i.e., through

orientation energy (OE), but making the decision globally.



Rotated copies of filters will pick up edge contrast at different orientations:

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Orientation Energy at orientation angle of 0

Orientation energy of a pixel located at (x,y)

Useful properties:

and form a quadrature pair.

Filters are elongated, information is integrated along the edge Extended contours will

stand out as opposed to short ones


Based on the presence of an extended contour, pixel pairs can be assigned to same or different partitions

OE is strong along l2 s1 and s3 are in different partitions.

OE is weak along l1 s1 and s2 are in the same partition.

Pairwise affinity matrix is formed using Eq. 10:


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Dissimilarity of two pixels


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Form affinity matrix (W)

Form affinity matrix for each channel based on OE

To account for proximity in the image plane calculate affinities only within a neighborhood.

PolSAR Data

Multi-looking Perform multi-looking on SLC data set

Segmentation of Polarimetric SAR Data Using Contour Information via Spectral Graph Partitioning

Perform the steps of Multiclass Spectral Clustering (MSC) algorithm by Yu and Shi.


Spectral Graph

Partitioning

Form affinity matrix W and perform SGP

Similarity is defined between segments obtained from the previous step. ( 2 – distance between the histograms is used)

Only consider adjacent segments

Proposed Scheme

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PolSAR Data

Multi-looking Perform multi-looking on SLC data set

Patch-basedsimilarity


Proximity

Form affinity matrix W for each data channel

To account for proximity in the image plane calculate affinities only within a neighborhood.

Contour information is measured using Orientation Energy (OE)

Perform the Spectral Graph Partitioning (SGP) using the Multiclass Spectral Clustering (MSC) algorithm.

Contour Information

SGP

SGP

Data Acquisition:

Convair-580, C - band, Sept. 2004

For the regions # 1 and # 2 the reference segmentation was formed by:

Inspection of the field boundaries and crops on the day of the acquisition

Visual interpretation of the image data

Manual Segmentation

Data Set: Westham Island, B.C.

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© CSA 2004



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For region #3 a classification map was formed using:

GPS measurements at the field boundaries

Inspection of the crops in each field on the day of the acquisition

Visual interpretation of the image data


© CSA 2004


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Pumpkin

Hay

Barley -1

Bare Soil

Potatoes

StrawberryTurnipBarley – 2

Segmentation of Polarimetric SAR Data Using Contour Information via Spectral Graph Partitioning

Results – Region # 1

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Wishart

6 fields

Wishart result contains isolated pixels

Proposed Method:

More homogenous

Visually agrees with reference segmentation



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8 fields

Wishart result contains isolated pixels

Proposed Method:

More homogenous

Visually agrees with reference segmentation

Wishart




13 different fields

Problems:

Adjacent fields with same crop type

Pumpkin Grass

Concave regions (Similarity calculation using OE suggests there should be two partitions

Non-adjacent fields with same crop type. (To be solved at the level of classification)

A new technique for segmentation of polarimetric SAR data is proposed

Motivated by the visual information content that humans utilize

Is based on SGP which was shown to perform well on computer vision problems

A pair-wise grouping technique instead of central grouping.

Contour cue and Proximity is used for initial partitioning

Patch-based similarity is used later to merge adjacent partitions

Summary

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Preliminary results are given on image subsets of Convair-580 data (C-band)

Perceptually plausible results: more homogenous, agree with the reference (i.e., manual) segmentation

Resulting classification is better than Wishart

This scheme is flexible to allow further improvement using additional information


Utilize the complete polarimetric information using pairwise similarity of the coherency matrices.

Include additional information (e.g., scattering mechanisms)

Optimize the cue combination scheme

Compare with techniques other than Wishart

Validate methodology for

Different datasets (CV-580)

RADARSAT-2

Future Work


Segmentation of Polarimetric SAR Data Using

Spectral Graph Partitioning: Utilizing Multiple Cues

Dept. of Electrical and Computer Engineering

University of British Columbia

Vancouver, Canada

Kaan Ersahin*, Ian Cumming and Rabab K. Ward

segmentation of polarimetric sar data using spectral graph partitioning: utilizing multiple cues...

Documents

shape x x x xo x

automated segmentation

multiple cues

sar polarimetry analysis

ward slide

visual aspect of image

human vision system

computer vision problems