pIGARSS.pdf
Transcript of pIGARSS.pdf
Classification of Multi-Source Images
using Color Morphological Profiles
V. De Witte, G. Thoonen, P. Scheunders,
A. Pizurica, W. Philips
IGARSS 2011
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Outline
• Introduction
• Color Morphological Profiles (CMPs)
• Experiments
• Conclusions
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Introduction
Goal:
Hyperspectralimage
RGB color image
Spectral features
Contextual features ?
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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
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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
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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
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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
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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
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Goal:
Hyperspectralimage
Spectral features
RGB color image
Contextual features using CMP
Color Morphological Profiles (4)
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Experiments: Washington DC Mall
DownscaledHyperspectral
RGB extracted from original
Concatenate
Extract CMP
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Classification using Support Vector Machines
Truecolor Image of Washington DC Mall
= high spatial resolution RGB color image
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Classification results
Truecolor ImageSpectral features from the
hyperspectral image
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Classification results
Spectral + CMPsTruecolor Image Spectral + MPs on R, G, B
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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%
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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
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Thank you!Questions?