MULTI-TEMPORAL OBJECT-ORIENTED

CLASSIFICATIONS AND URBAN

ANALYSIS. CASY STUDY BELO

HORIZONTE - MINAS GERAIS STATE

Dr. Hermann Johann Heinrich KuxMsc. Eduardo Henrique Geraldi Araújo

1st MULTIDISCIPLINARY WORKSHOP ON EXTRACTING

AND CLASSIFYING URBAN OBJECTS FROM HIGH

RESOLUTION SATELLITE IMAGES - 2007

Are our cities well known?

� Observation on the bad use of urban areas:

� Inadequate growth of urban areas with

great potential;

� Lack of information for adequate urban

planning activities.

Motivation for the study

Examples

� Evaluate the performance and characteristics of object-based image classifications as a contribution to urban planning and to sustainable development, by the presentation of a case study: Belo Horizonte, MG, Brazil.

Objective

� Characterization of area under study;

� Object Based Image Analysis, Data preparation and Classification results;

� Conclusions

Summary

� Characterization of area under study;

� Object Based Image Analysis. Data preparation and Classification results;

� Conclusions

Summary

Area under study

Image 2004

Image 2002

Buritis Belvedere

Study Area

Aspects of area under study

� Characterization of area under study;

� Object Based Image Analysis. Data preparation and Classification results;

� Conclusions

Summary

Methodological Procedures

Geological risks

Orthorectification

Analysis of:

- Requirements;

- Relevance;

- Membership

DEFINITION OF

AREA UNDER STUDY

Quickbird image 2002

Information acquired

Quickbird image 2004

Contour lineGEOMETRIC

CORRECTIOND-GPS (GCP)

DEM

Edited CadastreCLASSIFICATION

Data preparation

Definition of classes

Segmentation

Hierarchy

Membership rules

Evaluation

Ortho images

Ancillary data

Land cover Urban growthSPATIAL

INFERENCES

ANALYSIS OF

RESULTS

CONCLUSIONS Information generated

Field survey

Blocks

Streets

Outside

Ortho images and Cadastre

Data Preparation

High brightness. No discrimination of probable building materials. 10) White cover

Cover of large buildings. Presents many variations.9) Gray cover

Roofs, mainly of new asbestos and cement. Quantization level close to 2048.8) Flare

Strong response in the blue and sometimes in the green band. Well defined.7) Swimming pool

Low brightness. Close to high buildings and arboreal vegetation.6) Shadow

Used from urban cadastre to delimit streets, avenues and roads. Linear and

straight forms

5) Asphalt

Earth works, prepared for constructions. No standard. Irregular forms, variable

texture and random localization.

4) Bare soil

Simple square geometry, orange-like color with large tone variation. Smooth

texture. Easily identifiable visually.

3) Ceramics tile

Also strong response in the IR band. Uniform. Texture is smoother than

Arboreal vegetation.

2) Grass

Strong response in the IR band. Texture due to different heights of trees

(shadow). Easily distinguishable from other classes.

1) Arboreal vegetation

Descriptive CharacteristicsName of class

Definition of classes/Examples

Definition of Classes

1 1

2 2

3 3

4 4

55

66

77

88

99

10 10

RGB - 321 RGB - 432 RGB - 321 RGB - 432RGB - 321 RGB - 432

Definition of Classes

Segmentation

Level 2

Segmentation

Level 3

Level 2

Segmentation

Level 1

Hierarchical net

Classes considered

LEVEL 3 StreetsBlocks

No vegetation

AsphaltShadow No Shadow

High brightness No High brightness

FlareWhite cover Red objects No Red objects

Bare soil Ceramics tile Blue objects

Swimming poolGray cover

Vegetation

Grass Arboreal

Blue objects (L1) No Blue Objects (L1)

Ceramics tile (L1)

LEVEL 2

LEVEL 1

Outside area under study

Auxiliary classes

Membership Rules

RGB - 321

RGB - 321 Without NDVI rule With NDVI rule

Without texture rule With texture rule

Arboreal

X

Grass

Bare soil

X

Ceramics

Image ClassificationBelvedere 2004

Overall Kappa: 0,75

Overall Kappa: 0,86

Overall Kappa: 0,77 Overall Kappa: 0,74

Belvedere 2002

Buritis 2004

Buritis 2002

Areas of classes of land cover (in m2)

570190,35513661,34636175,84589856,07Impervious areas (total)

360869,06365222,18938670,19981459,79Vegetation (total)

99083,8887817,324576,329463,32Ceramics tile

182394,73242991,7388027,56125866,09Bare soil

52450,5642384,6092256,4858006,08Shadow

4590,002994,12626,76471,60Swimming pool

611,6493,60631,802331,36Flare

274347,02203189,77444673,83412826,79Gray cover

30020,4052663,3252157,1645323,28White cover

166739,05169990,93134768,53122242,68Asphalt

Belvedere 2004Belvedere 2002Buritis 2004Buritis 2002Thematic Classes

Urban Growth

Spatial Inferences

Spatial Inferences

Spatial Inferences

Buritis

Belvedere

Low Vulnerability High

Irregular occupations

Vulnerability

� Characterization of area under study;

� Object Based Image Analysis. Data preparation and Classification results;

� Conclusions

Summary

From the results found we conclude that:

�The evaluation of classifications indicated the possibility to use the land

cover map in this study, especially for the spatial inferences;

�The study showed the possibility to map areas with tendencies of growth

and risks, besides being a reliable information source for urban planning;

�The object-based image analysis methodology is a valid approach for the

classification of such large datasets as the Quickbird satellite images used

in this study. Taking into account the large volume of high resolution

sensor systems and the resulting enormous datasets available in the next

few years, it is advisable to improve this approach by all means, so it

becomes an operational tool for e.g. urban planning agencies, as

indicated in previous application examples.

Conclusions

Conclusions

In order to improve this methodology specifically for urban applications, we

strongly recommend the following further studies:

� To evaluate the use of high-resolution images, obtained at several

incidence angles. Use preferentially complementary angles, so it is possible

to generate stereoscopy to obtain information of land/soil cover in occlusion

areas;

�To test the efficiency on the use of the DSM for the distinction of bare soils

and ceramic tiles;

�To investigate descriptors that are able to discriminate classes of gray

cover like asbestos, metallic roofs and cement, quite frequently used in

Brazil;

�To map objects of several classes which are in the shadow.

Last but not least, to improve the OBIA approach, absolutely indispensable

for the “flood” of data which will become available from the large number of

high-resolution spaceborne sensors to be launched in the next few years.

THANK YOU !