Fuzzy Regions for Handling Uncertainty in Remote Sensing Image Segmentation Ivan Lizarazo, (a) and Paul Elsner (b) (a) Department of Cadastral Engineering.

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Presentation transcript:

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

3 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

4 Introduction (2) Geographic Object-based Image Analysis: Fuzzy Regions Attributes Assessment Pre-processed pixels Image Objects Attributes Vector Segmentation Classification Ground Objects

5 Introduction (3) Discrete Image Segmentation: Image is subdivided into discrete objects with well defined boundaries Fuzzy Regions A B C Input image Segmented image

6 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

7 Introduction (5) Continuous Image Segmentation: Image is subdivided into “fuzzy” objects with degrees of membership to classes I. Lizarazo Segmented image A B C Input image

8 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

9 Hydice Imagery I. Lizarazo

10 Methods Fuzzy Regions Attributes Assessment Pre-processed Pixels Image Regions Attributes Vector Fuzzy Segmentation Classification Land-cover Classes

11 Methods … Fuzzy Regions Attributes Assessment Pre-processed Pixels Image Regions Attributes Vector Fuzzy Segmentation Classification Land-cover Classes Support Vector Machines Contextual Indices Defuzzification Principal Components Analysis

12 Methods: Segmentation Fuzzy Regions Support Vector Machine (SVM) Given training data (x i, y i ) find a function f(x) that has at most ε deviation from the targets y i Transformation of the original space into a higher dimension using a kernel function k(x,x i )

13 Methods: Segmentation I. Lizarazo SVM Kernel: Radial Basis Function Automated SVM parameterization: Implementation: libsvm (R package)

14 Methods: Attribute Assessment Fuzzy Regions - Overlapping Index (Lambert and Grecu, 2003): - Confusion Index (Burrough et al, 1997):

15 Methods: Defuzzification I. Lizarazo Fuzzy Regions Land-cover Classes SVM-based Classification Fuzzy Regions Intensified Fuzzy Union Operation CL-1 CL-2 CL-3

16 Methods: Defuzzification I. Lizarazo Fuzzy Regions Land-cover Classes Fuzzy Union Operation CL-1

17 Methods: Defuzzification I. Lizarazo Fuzzy Regions Land-cover Classes Fuzzy Regions Intensified CL-3 SVM-based Classification

18 Methods: Defuzzification I. Lizarazo CL2 - CL3 SVM-based classification: There is a separating hyperplane which maximises the margin between classes

19 Results: Fuzzy Image-Regions I. Lizarazo Road Roof Shadow OI

20 Results: Fuzzy Image-Regions … I. Lizarazo GrassTrees WaterTrail

21 Results: Land-cover classification Fuzzy Regions CI CL-1 CL-2Reference

22 Results: Classification Accuracy Fuzzy Regions CL-2 Percentage of Correct Classification = 87%

23 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?