5 igarss2011_mdm.pdf

A General Approach to the Spatial Simpli�cation of Remote Sensing

Images Based on Morphological Connected Filters

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

†Department of Information Engineering and Computer ScienceUniversity of Trento.

�Faculty of Electrical and Computer EngineeringUniversity of Iceland.

IGARSS 2011

24-29 July

Outline

1 Introduction

2 General Approach for Image Simpli�cation

Connected Operators

Methodology

3 Conclusion and Future Developments

IGARSS 2011 (24-29 July) Mauro Dalla Mura [email protected] 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 Simpli�cation

Spatial simplication of the image.

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

Image simpli�cation leads to:I noise reduction;I exploiting the contextual relations;I modeling spatial relations;I removing or attenuating undesired details.

Simpli�cation of the image performed by spatial �ltering, a 2-step procedurecomposed of:

1 selection of the �lters parameters;2 application of the operator on the image.


Introduction

Image Simpli�cation (examples)

VHR optical image - di�erent simpli�cations

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 simpli�cation

The selection of the parameters of the �lters is application dependent.

Proper operators should be used.

Manual operation.

Aims of the work

De�ne a novel general approach to image simpli�cation

based on morphological connected operators;

suitable for the processing of di�erent types of images and di�erent

applications;

suitable to be performed in an automated way.


General Approach for Image Simpli�cation 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

φB(f )

Closing by rec.

φBR(f ) = Rε

f[δB(f )]

Original image

f

Opening by rec.

γBR(f ) = Rδ

f[εB(f )]

Opening

γB(f )

Connected operators are suitable for the analysis of VHR images.


General Approach for Image Simpli�cation Connected Operators

Connected Operators - Attribute Filters

Attribute �lters are connected operators de�ned 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 ful�ll a condition (T )

which compares an attribute attr against a reference value λ

(e.g., T = attr ≥ λ).Main operators:I attribute thinning, γT ;I attribute thickening, φT .

Example: Di�erent attributes

Original image Area Standard deviation Moment of inertia Solidity

1 M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, �Morphological attribute pro�les for the analysisof very high resolution images,� IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 10, pp.3747 � 3762, Oct. 2010.IGARSS 2011 (24-29 July) Mauro Dalla Mura [email protected] 8 / 19

General Approach for Image Simpli�cation Methodology

Architecture of the Proposed General Approach

Scenario 3

ApplicationKnowledge

XParameters Selection

Y

SceneKnowledge

Scenario 1

Scenario 2

Bank ofConnected Filters

e. g., attribute thinning,attribute thickening, ...

PerformanceAssessment

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

of:

1 selection of the parameters and operators;

2 �ltering.



Filter Selection and Operative Scenarios

Operative Scenarios

Information available as prior

knowledge:

1 Scenario 1Scene Knowledge %

Application Knowledge %

2 Scenario 2Scene Knowledge %

Application Knowledge "

3 Scenario 3Scene Knowledge "

Application Knowledge "

Scenario 3

ApplicationKnowledge

XParameters Selection

Y

SceneKnowledge

Scenario 1

Scenario 2

Bank ofConnected Filters

e. g., attribute thinning,attribute thickening, ...

PerformanceAssessment



Data set

Panchromatic image

Quickbird

panchromatic

image of 995×995pixels, 0.6 m

resolution.

Acquired over a

residential urban

area of Bam, Iran.



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 signi�cantly di�erent

from their surroundings;2 Inter-object variability ⇒ Flattening small values di�erences 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 simpli�cation performed by:

γTφT with area attribute (remove small bright and dark regions);

γT with T based on relations between the regions1 (merge nested regions).

VHR image

Building rooftop (80×60 pixels). 2789 �at regions. Simpli�ed 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:I Application: building extraction.I Aim of the simpli�cation: enhance rectangular regionsI Concept: �keep rectangular regions�.I 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. f − γBR(f ) (B: disk radius 7 pixels)

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



Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)


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

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



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 simpli�cation obtained can be

evaluated on the known samples according to a given criterion.

Automation

De�ne a cost function to minimize, representing the �tness of the generated

result with the input requirements;

De�ne a optimization procedure and a stopping condition.

See on Thurstday: S. Peeters, P. R. Marpu, J. A. Benediktsson, M. Dalla Mura �Classi�cation using extended

morphological attribute pro�les based on di�erent feature extraction techniques.�


Conclusion and Future Developments


Conclusion

De�nition of a novel general approach for image simpli�cation based on

connected �lters (in particular attribute �lters).Suitable forI processing many di�erent image types;I di�erent applications involving image analysis.

Contributions:I identi�cation of three scenarios modeling common di�erent operative

conditions;I giving guidelines for the automation of the process;I qualitative evaluation of the proposed approach on a real data set in di�erent

scenarios.

Future Developments

Extensively test the approach on di�erent type of images and applications.

Improve the automation of the process.



Thanks for your attention!


5 igarss2011_mdm.pdf

Technology

type of image

panchromatic image

hyperspectral image

remote sensing images

undesired details

pansharpened images

remote sensing applications

huge amountof details