Classification of Multi-Source Images

using Color Morphological Profiles

V. De Witte, G. Thoonen, P. Scheunders,

A. Pizurica, W. Philips

IGARSS 2011

1

Outline

• Introduction

• Color Morphological Profiles (CMPs)

• Experiments

• Conclusions

2

Introduction

Goal:

Hyperspectralimage

RGB color image

Spectral features

Contextual features ?

3

Morphological Profiles

Structural element 3

Structural element 2

Structural element 1

MorphologicalProfile

Morphological profile = a series of openings and closings by

reconstruction with increasing structuring element

Panchromaticimage

4

Extended Morphological Profiles

PC3

PC1PC2

Principal component

analysis

J. A. Palmason, J. A. Benediktsson, J. R. Sveinsson, and J. Chanussot, “Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis,” in International Geoscience and Remote Sensing Symposium (IGARSS), July 2005, pp. 176 – 179.

Extended Morphological Profile

Hyperspectralimage

5

Color Morphological Profiles (1)

GOAL: generate a MP from high-resolution RGB color image

• Extension Grayscale morphology to Color Morphology

Component-based approach leads to artifacts: existing correlation between the different color bands is lost

Define vector ordering ≤RGB

Contrary to EMP, the color bands are NOT treated separately

V. De Witte, S. Schulte, E. Kerre “New Vector Ordering in the RedGreenBlue color model with application to morphological image magnification,” in International Journal of Computational Intelligence Systems, vol. 1, no. 2, 103-115, May 2008

6

Color Morphological Profiles (2)

Definition of ≤RGB:

• Colors in RGB are sorted from dark to light, with respect to their distance to the centre m(1/2,1/2,1/2) of the RGB cube (as the middle of the black and white top)

• Associated min and max operators are defined

Black

White

7

Color Morphological Profiles (3)

• Morphological operators are extended to vector-based operators acting on color images

• Color Morphological Profile CMP

each morphological operator is 3-dimensional

CMP is a 3x (2n+1)-dimensional feature vector

8

Goal:

Hyperspectralimage

Spectral features

RGB color image

Contextual features using CMP

Color Morphological Profiles (4)

9

Experiments: Washington DC Mall

DownscaledHyperspectral

RGB extracted from original

Concatenate

Extract CMP

10

Classification using Support Vector Machines

Truecolor Image of Washington DC Mall

= high spatial resolution RGB color image

11

Classification results

Truecolor ImageSpectral features from the

hyperspectral image

12

Classification results

Spectral + CMPsTruecolor Image Spectral + MPs on R, G, B

13

Classification results

Accuracy for Washington DC Mall data set:

Overall accuracy Kappa coefficient

Spectral 84.7% 78.4%

Spectral + RGB 90.3% 85.9%

Spectral + EMP 93.4% 90.2%

Spectral + CMP 93.9% 90.9%

14

Conclusions

• Color Morphological Profiles are defined

extracted from a high spatial resolution color image

the RGB color components are treated as vectors, thereby avoiding the introduction of artefacts

• Combine with spectral information

• Proposed method shows very good results

15

Thank you!Questions?