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The Use of Ikonos Image and Object
Oriented Classification in the cutaenous
leishmaniasis Study
Introduction
What is the cutaneous leishmaniasis?
Infectious disease caused by protozoans of leishmania gender
-Characteristics: disease with ample spatial distribution; high
incidence of cases per year; attacks the skin, in some situations,
progressing to mutilate forms
- Transmitter: phlebotomine sandfly – Insects belonged to many
species and different genders
-Transmission Standards : Sylvester, Intermediary and Peridomestic
Introduction
Peridomestic cutaneous leishmaniasis (CL)
Favorable areas to concentration of the transmitter
EcotopesContact areas
between forest and rarefy vegetation
=
Introduction
Areas with potential risk transmission of
Peridomestic CL to human population
HumanDomiciles
Ecotopes
Objective
• Application of Object Oriented Classification to
identify the variables to define the Areas with
potential risk transmission of CL:
- Forest Areas
- Areas with rarefy trees
- Areas with presence of population
Study Areas
• two scene components of satellite Ikonos II: one for the methodology development area (13/03/02) and other to the validation area (12/04/2002)
• one digital cartographic basis (1999) - scale 1/10.000
• Digitals Orthophotos - scale 1:2.000; 1999.
• Softwares: ArcGis v.9, PCI v.10, and eCognition 4.0
Materials
IKONOS II – Image Preparation:
Orthorectification
• Verification about the necessity of realize the
orthorectification:
a) Register of the IKONOS II scene components according
to the cartographic digital basis.
b) Despite the images register: displacements between 20
and 100 meters in relation to the basis.
• DEM Generation and Orthorectification
a) Altimetry data import to PCI software and DEM
generation
b) DEM + RPC file => Image orthorectification
IKONOS II – Image Preparation:
Orthorectification
Before Orthorectification: displacement between 20 and 100 m
IKONOS II – Image Preparation:
Orthorectification
After Orthorectification: Displacements between 1 and 2 m
IKONOS II – Image Preparation:
Orthorectification
• Importance of the Panchromatic Band:
a) Identify isolated smalls constructions.
b) More texture detail in vegetal cover areas
• Importance of the Multispectral Bands
a) To describe different kinds of vegetation.
IKONOS II – Image Preparation:
Fusion Process
• Fusion Process realized using Pansharp algorithm - PCI
a) Works with 11 bits, preserving the 2048 Digitals
Numbers.
b) Preserves the colors: tests realized – Statistical
Parameters Analysis; Histograms Analysis and Visual
Analysis.
IKONOS II – Image Preparation:
Fusion Process
IKONOS II
Segmentation and Classification
• Defined Classes:
a) Dense Trees (Forest)
b) Rarefy Trees
c) Edifications
• Segmentation:
a) two levels: one for Edifications Class and other for
vegetation classes (Forest and Rarefy Trees)
• Segmentation:
a) First Level: Scale Parameter: 35; Shape: 0.3; Color: 0.7;
Compactness: 0.5 and Smoothness: 0.5
b) Second Level: Scale Parameter: 110; Shape: 0.3; Color:
0.7; Compactness: 0.5 and Smoothness: 0.5
IKONOS II
Segmentation and Classification
• Segmentation:
First Level Second Level
IKONOS II
Segmentation and Classification
• Classification:
a) Application of the Nearest Neighbor (NN) Classifier
b) First Level:
- Classes: Edifications and Not Edifications
- Describers Used: Mean Spectral Value and Compactness
c) Second Level:
- Classes: Dense Trees; Rarefy Trees; Creeping Vegetation and Others
- Describers Used: Mean Spectral Value; Compactness and Texture
IKONOS II
Segmentation and Classification
• Classification:
d) Application of the classification process more than one time:
necessity to add and exclude samples
e) Exportation of the objects belonged to classes of interest
(Edifications; Dense Trees and Rarefy Trees) in a SHP format
f) Editions in the ArcMap software with the purpose to exclude the
classification errors
g) Results Verification: Classification Ambiguity Analysis and
Accuracy Analysis
IKONOS II
Segmentation and Classification
Results - Classification
• Methodology Development Area - Original and Classified Image
• Development Area – Ambiguity Analysis
a) Dense Trees: 0,362 – “Acceptable”
b) Rarefy Trees: 0,380 – “Acceptable”
c) Edifications: 0,951 – “Very Good”
CLASSIFICAÇÃO UNACCEPTABLE Ia = 0;CLASSIFICAÇÃO AMBIGUOUS 0,01 ≤ Ia ≤ 0,30;CLASSIFICAÇÃO ACCEPTABLE 0,31 ≤ Ia ≤ 0,50;CLASSIFICAÇÃO GOOD 0,51 ≤ Ia ≤ 0,80;CLASSIFICAÇÃO VERY GOOD 0,81 ≤ Ia ≤ 1.
Linguistic Scale (ANTUNES e LINGNAUL, 2005)
Results - Classification
• Development Area – Kappa
Linguistic Scale (LANDIS e KOCH, 1977)
Global Kappa = 0,81 => HIGH Correlation
HIGH CORRELATION = > 0.80SUBSTANTIAL CORRELATION = 0.60 a 0.79MODERATE CORRELATION = 0.40 a 0.59SMALL CORRELATION = 0.20 a 0.39LOW CORRELATION = 0.00 a 0.19NO CORRELATION = < 0.00
Results - Classification
Results - Classification
• Methodology Validation Area - Original and Classified Image
a) Dense Trees: 0,335 – “Acceptable”
b) Rarefy Trees: 0,506 – “Good”
c) Edifications: 0,829 – “Very Good”’
Results - Classification
• Validation Area – Ambiguity Analysis
CLASSIFICAÇÃO UNACCEPTABLE Ia = 0;CLASSIFICAÇÃO AMBIGUOUS 0,01 ≤ Ia ≤ 0,30;CLASSIFICAÇÃO ACCEPTABLE 0,31 ≤ Ia ≤ 0,50;CLASSIFICAÇÃO GOOD 0,51 ≤ Ia ≤ 0,80;CLASSIFICAÇÃO VERY GOOD 0,81 ≤ Ia ≤ 1.
Linguistic Scale
Global Kappa = 0,76 => SUBSTANTIAL Correlation
• Development Area – Kappa
Linguistic Scale
HIGH CORRELATION = > 0.80SUBSTANTIAL CORRELATION = 0.60 a 0.79MODERATE CORRELATION = 0.40 a 0.59SMALL CORRELATION = 0.20 a 0.39LOW CORRELATION = 0.00 a 0.19NO CORRELATION = < 0.00
Results - Classification
The Ambiguity and Accuracy analysis showed that the
IKONOS images associated with Object Oriented
Classification can be used, with security, to generate
a product which can be applied in the study of
Peridomestic CL
Conclusion
© Threetek Ltda. 2006
Rua México, 41 / 17° andar - Centro
20031-144 - Rio de Janeiro- RJ - Brasil
Tel/Fax: (21) 2524-0207
Guilherme Medina
Geógrafo- MSc. Engenharia Cartográfica, IME
OBRIGADO
