fuzzy regions for handling uncertainty in remote sensing image segmentation ivan lizarazo, (a) and...

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

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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?