fuzzy regions for handling uncertainty in remote sensing image segmentation ivan lizarazo, (a) and...
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Fuzzy Regions for Handling Uncertainty in Remote Sensing Image Segmentation
Ivan Lizarazo, (a) and Paul Elsner (b) (a) Department of Cadastral Engineering
University Distrital, Bogota, Colombia
(b) School of Geography, Birkbeck College, University of London, UK
Agenda
1. Introduction
2. Case Study: Urban land-cover classification
3. Results
4. Conclusions
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Introduction
Geographic Object-based Image Analysis:
- alternative to pixel-wise classification. - includes contextual and geometric information.
- key steps: (1) group pixels into segments. (2) evaluate segment’s properties.
Fuzzy Regions
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Introduction (2)
Geographic Object-based Image Analysis:
Fuzzy Regions
Attributes Assessment
Pre-processed pixels
Image Objects
Attributes Vector
Segmentation
Classification
Ground Objects
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Introduction (3)
Discrete Image Segmentation:
Image is subdivided into discrete objects with well defined boundaries
Fuzzy Regions
A
BC
Input image Segmented image
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Introduction (4)
Problems of Discrete Image Segmentation:
• Noisy images and pixel mixed may produce meaningless image-objects.
• Geographic objects are not always discrete features.
• Establishing a correspondence between image-objects and real-world objects is a time-consuming process.
I. Lizarazo
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Introduction (5)
Continuous Image Segmentation: Image is subdivided into “fuzzy” objects
with degrees of membership to classes
I. LizarazoSegmented image
A
B
CInput image
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Case Study: Classification of urban land-cover
Fuzzy Regions
Geographic Area: Washington DC-Mall
Data: HYDICE Imagery– 191 spectral bands– 3 meters spatial resolution– 1280 x 307 pixels
Ground Reference:– Training dataset: 704 pixels– Testing dataset: 1193 pixels
http://cobweb.ecn.purdue.edu/~landgreb/Hyperspectral.Ex.html
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Hydice Imagery
I. Lizarazo
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Methods
Fuzzy Regions
Attributes Assessment
Pre-processed Pixels
Image Regions
Attributes Vector
Fuzzy Segmentation
Classification
Land-cover Classes
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Methods …
Fuzzy Regions
Attributes Assessment
Pre-processed Pixels
Image Regions
Attributes Vector
Fuzzy Segmentation
Classification
Land-cover Classes
SupportVectorMachines
Contextual Indices
Defuzzification
Principal Components Analysis
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Methods: Segmentation
Fuzzy Regions
Support Vector Machine (SVM)
• Given training data (xi, yi) find a function f(x) that has at most ε deviation from the targets yi
•Transformation of the original space into a higher dimension using a kernel function k(x,xi)
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Methods: Segmentation
I. Lizarazo
SVM Kernel: Radial Basis Function
Automated SVM parameterization:
Implementation: libsvm (R package)
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Methods: Attribute Assessment
Fuzzy Regions
- Overlapping Index (Lambert and Grecu, 2003):
- Confusion Index (Burrough et al, 1997):
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Methods: Defuzzification
I. Lizarazo
Fuzzy Regions
Land-cover Classes
SVM-basedClassification
Fuzzy RegionsIntensified
Fuzzy UnionOperation
CL-1
CL-2
CL-3
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Methods: Defuzzification
I. Lizarazo
Fuzzy Regions
Land-cover Classes
Fuzzy UnionOperation
CL-1
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Methods: Defuzzification
I. Lizarazo
Fuzzy Regions
Land-cover Classes
Fuzzy RegionsIntensified
CL-3
SVM-basedClassification
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Methods: Defuzzification
I. Lizarazo
CL2 - CL3 SVM-based classification: There is a separating hyperplane which maximises the margin between classes
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Results: Fuzzy Image-Regions
I. Lizarazo
Road Roof Shadow OI
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Results: Fuzzy Image-Regions …
I. Lizarazo
Grass Trees Water Trail
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Results: Land-cover classification
Fuzzy Regions
CI CL-1 CL-2 Reference
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Results: Classification Accuracy
Fuzzy Regions
CL-2
Percentage of Correct Classification = 87%
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Conclusions
I. Lizarazo
• Fuzzy Image Segmentation: alternative for handling ambiguous information
• Automated SVM parameterisation may help users to produce accurate classifications • R provides useful functionalities for remote sensing image analysis
Questions?