5 igarss2011_mdm.pdf

A General Approach to the Spatial Simplication of Remote Sensing
Images Based on Morphological Connected Filters

Mauro Dalla Mura
†, , Jón Atli Benediktsson , Lorenzo Bruzzone†

† Department of Information Engineering and Computer Science
University of Trento.

Faculty of Electrical and Computer Engineering
University of Iceland.

IGARSS 2011
24-29 July

Outline

1 Introduction

2 General Approach for Image Simplication
Connected Operators
Methodology

3 Conclusion and Future Developments

IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 2 / 19

Introduction

Remote Sensing VHR Images

QuickBird 60cm, Panchromatic image, Bam (Iraq) Geoeye 50cm, Pansharpened images, Vancouver (Canada)
c Google

ROSIS-03 1.3m, Hyperspectral image, Pavia (Italy) TerraSAR-X 1.1m, Spotlight SAR, Dorsten (Germany)

The information extraction in remote sensing images is becoming increasingly
complex due to the progressively higher spatial resolution of the current
sensors.
How to extract the informative components dealing with the huge amount
of details?

Introduction

Image Simplication

Spatial simplication of the image.

Pre-processing operation for many remote sensing applications:
Image segmentation;
Supervised/unsupervised thematic classication;
Land cover change analysis;
Object recognition and extraction;
Denoising SAR images;
Analysis of multiangular images.

Image simplication leads to:
noise reduction;
exploiting the contextual relations;
modeling spatial relations;
removing or attenuating undesired details.

Simplication of the image performed by spatial ltering, a 2-step procedure
composed of:
1 selection of the lters parameters;
2 application of the operator on the image.


Introduction

Image Simplication (examples)

VHR optical image - dierent simplications

Which details should be removed?
Which operator should be applied? And which lter parameters should be
selected?
It depends on the application and on the type of image.


Introduction

Motivation

Issues related to image simplication
The selection of the parameters of the lters is application dependent.
Proper operators should be used.
Manual operation.

Aims of the work
Dene a novel general approach to image simplication
based on morphological connected operators;
suitable for the processing of dierent types of images and dierent
applications;
suitable to be performed in an automated way.


General Approach for Image Simplication Connected Operators

Connected Operators - Operators by Reconstruction
Connected operators are morphological lters that process an image by only
merging its at zones.
Either completely remove or entirely preserve a region in the image.
They do not distort the geometrical characteristics (e.g., shape, edges) of the
structures in the image.

Operators by Reconstruction
Closing Closing by rec. Original image Opening by rec. Opening

φB (f ) φB (f ) = Rf [δB (f )]
R
ε f B δ
γR (f ) = Rf [εB (f )] γB (f )



Connected Operators - Operators by Reconstruction
Connected operators are morphological lters that process an image by only
merging its at zones.
Either completely remove or entirely preserve a region in the image.
They do not distort the geometrical characteristics (e.g., shape, edges) of the
structures in the image.

Operators by Reconstruction
Closing Closing by rec. Original image Opening by rec. Opening

φB (f ) φB (f ) = Rf [δB (f )]
R
ε f B δ
γR (f ) = Rf [εB (f )] γB (f )

Connected operators are suitable for the analysis of VHR images.


Connected Operators - Attribute Filters
Attribute lters are connected operators dened in the mathematical
morphology framework and recently used for the analysis of remote sensing1 .
Based on a measure (attribute) computed on the regions of an image.
Filtering performed by removing the regions that do not fulll a condition (T )
which compares an attribute attr against a reference value λ
(e.g., T = attr ≥ λ).
Main operators:
attribute thinning, γT ;
attribute thickening, φT .

Example: Dierent attributes

Original image Area Standard deviation Moment of inertia Solidity

1 M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, Morphological attribute proles for the analysis
of very high resolution images, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 10, pp.
IGARSS 20113762, Oct. 2010.
3747 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 8 / 19

General Approach for Image Simplication Methodology

Architecture of the Proposed General Approach

X
Parameters Selection

Bank of
Scenario 1 Y
Connected Filters
e. g., attribute thinning,
attribute thickening, ...
Application
Scenario 2
Knowledge

Scene Performance
Knowledge Scenario 3 Assessment

The proposed approach is composed of two modules that perform the operations
of:




X

Bank of
Scenario 1 Y
Connected Filters
Application
Scenario 2
Knowledge

Scene Performance

of:
1 selection of the parameters and operators;




X

Bank of
Scenario 1 Y
Connected Filters
Application
Scenario 2
Knowledge

Scene Performance

of:
1 selection of the parameters and operators;
2 ltering.



Filter Selection and Operative Scenarios

Operative Scenarios
Information available as prior
knowledge: X
1 Scenario 1
%
Scene Knowledge Scenario 1
Bank of
Y

%
Connected Filters
Application Knowledge e. g., attribute thinning,

2 Scenario 2 Application

%
Scene Knowledge
Knowledge
Scenario 2

Application Knowledge

3 Scenario 3 Scene
Scenario 3
Performance

Knowledge Assessment
Scene Knowledge

Application Knowledge



Data set

Quickbird
panchromatic
image of 995×995
pixels, 0.6 m
resolution.
Acquired over a
residential urban
area of Bam, Iran.

Panchromatic image



Scenario 1 - Application Knowledge %, Scene Knowledge %

Aim: Generic reduction of the complexity of the image by reducing non
informative components.
Filtering aiming at reducing:
1 Noisy components ⇒ Removing small regions with values signicantly dierent
from their surroundings;
2 Inter-object variability ⇒ Flattening small values dierences in homogeneous
regions.

Suitable to cope with most of the applications.
Eases the interpretation of the scene.
Exploits the contextual relations of the pixels.
Fully automatic suitable for batch processing.



Scenario 1 - Example

Generic simplication performed by:
γ T φT with area attribute (remove small bright and dark regions);
γ T with T based on relations between the regions1 (merge nested regions).

VHR image

Building rooftop (80×60 pixels). 2789 at regions. Simplied image. 1059 regions.

1 V. Caselles and P. Monasse, Geometric Description of Images as Topographic Maps. Springer, 2010.



Scenario 2 - Application Knowledge , Scene Knowledge %

Filters parameters selected according to the application.
The translation of the characteristics of the objects of interest from the
concept to the lter parameters.
Example:
Application: building extraction.
Aim of the simplication: enhance rectangular regions
Concept: keep rectangular regions.
Filtering: attribute lter with criterion: {rectangularity 0.5});

Automation
Modeling the range of values of the features that drive the lters with fuzzy
possibilistic functions.
Defuzzify in order to get the values for the lters parameters.



Scenario 2 - Enhancement of Buildings (Particulars)

Panchromatic image (f )

Filter by rec.
B
f − γR (f ) (B : disk radius 7 pixels)

Attribute lter γ T (f ) with T = (R 0.3) ∧ (I 0.5) ∧ (50 A 5000)
R: rectangularity; I: moment of inertia; A: area





Filter by rec.
B




Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)


Filter by rec. C[φB (f ) − f ] (B :
R
disk radius 3 pixels)

Attribute lter φT (f ) with T = (H 10000) ∧ (I 1.0)
H: height; I: moment of inertia





R




Scenario 3 - Application Knowledge , Scene Knowledge

Ad hoc parameters selection. If the available information is a set of labeled
samples (i.e., a training set), the reduction of the image complexity
generated by the ltering aims at increasing the separability of the classes.
Performance assessment. The quality of the simplication obtained can be
evaluated on the known samples according to a given criterion.

Automation
Dene a cost function to minimize, representing the tness of the generated
result with the input requirements;
Dene a optimization procedure and a stopping condition.
See on Thurstday: S. Peeters, P. R. Marpu, J. A. Benediktsson, M. Dalla Mura Classication using extended

morphological attribute proles based on dierent feature extraction techniques.


Conclusion and Future Developments


Conclusion
Denition of a novel general approach for image simplication based on
connected lters (in particular attribute lters).
Suitable for
processing many dierent image types;
dierent applications involving image analysis.

Contributions:
identication of three scenarios modeling common dierent operative
conditions;
giving guidelines for the automation of the process;
qualitative evaluation of the proposed approach on a real data set in dierent
scenarios.

Future Developments
Extensively test the approach on dierent type of images and applications.
Improve the automation of the process.



Thanks for your attention!


5 igarss2011_mdm.pdf

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