A Bragg grating-based imager for spectral analysis in urban scenes · 2012. 9. 27. · Motivation A...
Transcript of A Bragg grating-based imager for spectral analysis in urban scenes · 2012. 9. 27. · Motivation A...
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Colour Imaging Laboratory(www.ugr.es/local/colorimg)
A Bragg grating -based imager for spectral analysis in urban scenes
Aida Rodríguez, Juan Luis Nieves*, Eva Valero, Javier Hernández-Andrés, Javier Romero
Department of Optics
University of Granada (SPAIN)
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Motivation
� A Bragg grating-based spectral imager to obtain spectral reflectances at a pixel in the visible and near infrared.
� Explore possibilities of spectral segmentation using Fuzzy C-means.
� Appropriate metric to help spectral clustering and spectral categorization in urban scenes.
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… and on surface relief.
Introduction
Object colors depend on both, the spectral reflectance ofthe surfaces and the spectral power distribution of the lightimpinging on them…
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Eye as the receptor……the human retina has 3 types of
cone cells and 1 type of rod cells.
• Univariance principle: there is no information in the response of a single photoreceptor about the wavelength of the light which affects it.
3 types of cones: trichromatic vision
Introduction
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Eye or a digital camera as the receptor…
Univariance principle in imaging systems
)(λR
)(λG)(λB
∫=nm
nm
dER
780
380
)()(R λλλ
∫=nm
nm
dEG
780
380
)()(G λλλ
∫=nm
nm
dEB
780
380
)()(B λλλ
3 types of receptors: trichromatic image capture
Introduction
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Spectral approach vs. colorimetric approach
• Metamerism is avoided;• Illuminant changes can be reliably
simulated;
• How do dichromats see?
• Other applications in remote sensing, agriculture, astronomy, medicine, art restoration, cosmetics, printing, etc.
Introduction
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Calibrated dispersive devices
Typicalspectral
measurement configurations
Limited FOV
400 500 600 7000
0.1
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0.7
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Wavelength (nm)
Spectral image acquisition
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� Better resolution than conventional spectroradiometers
� Easy and cheaper
Spectral image acquisition
)(λE
400 500 600 7000
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Wavelength (nm)
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Different approaches
Liquid Crystal Tunable Filtre (LCTF)
A CCD camera with a narrow -band filtre set
coupled or LCTF
Spectral image acquisition
Multispectral system with LCTF
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Spectral image acquisition
Spectral line cameras
Different approaches
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Filtre wheel
n colour filtres
Different approaches
A CCD camera through broad-band colour filtres
some “a priori” information+
Spectral image acquisition
Filter wheel based cameras
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Bragg grating -based spectral imager
The double Volume Bragg Grating based device is able to select a single wavelength for each pixel in a full camera field (from 400 to 1000 nm).
Spectral image acquisition
Different approaches
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( ) ( )700
400
1/ 2 1/ 2700 7002 2
400 400
( ) ( )GFC
( ) ( )
r
r
f f
f f
λ
λ λ
λ λ
λ λ=
= =
= ∑
∑ ∑
� CIELab colour difference: colorimetrically acceptable if <3 CIELab
� Goodness-of-Fit-Coefficient (GFC): colorimetric accurate fit >0.995good spectral fit >0.999almost exact fit >0.9999
� Root-Mean-Square-Error (RMSE),acceptable if 2%-3%
Spectral and colorimetric evaluation metrics
*(1 1000(1 ))ab
CSCM Ln GFC E= + − + ∆
� A single cost function,
reference values of 3-4 units.
Multidimensional problem…
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Bragg grating -based spectral imager
640 nm 800 nm
Acquisition Time: 10 min. (580 images)
Exposure Time: 0.4 seconds
Spectral Range: 420nm to 1000nm
Spectral Resolution: 2nm
Methods
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� A Bragg grating-based spectral imager to obtain spectral reflectances at a pixel in the visible and near infrared.
� Explore possibilities of spectral segmentation using Fuzzy C-means.
� Appropriate metric to help spectral clustering and spectral categorization in urban scenes.
Motivation
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Hyperspectral image dataset
Real spectral images: urban scenes
Methods
False-color synthetic hyperspectral images.
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Fuzzy C-Means (FCM) clustering
Methods
Take advantage of spectral information and adapt classical clustering to the image data provided by a spectral imager.
Partition a set of feature vectors Xinto K clusters (subgroups) represented as fuzzy sets F1, F2, …, FKby minimizing the objective function Jq(U,V):
Jq(U,V) = ΣiΣk(uik)qd2(Xj – Vi); K ≤ N
Larger membership values indicate higher confidence in the assignment of the member to the cluster.
using an Euclidean distance
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Spectral Similarity Value (SSV)
FCM with SSV distance metric
to create spectrally more homogeneous clusters and so obtain a better performance in segmentation of hyperspectral images.
Metric designed for quantitative comparison of two spectra and to take into account both magnitude and spectral-shape differences; it combines an Euclidean distance-based term and a Pearson correlation-based term.
The range of this distance metric is between zero and the square root of two.
Methods
, and
where:
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Spectral Similarity Value (SSV)
FCM with SSV distance metric
to create spectrally more homogeneous clusters and so obtain a better performance in segmentation of hyperspectral images.
Metric designed for quantitative comparison of two spectra and to take into account both magnitude and spectral-shape differences; it combines an Euclidean distance-based term and a Pearson correlation-based term.
The range of this distance metric is between zero and the square root of two.
Methods
Similar shapes and
very different in
scale:
de= 0.4035;
SSV= 0.4097
Similar scale and
dissimilar spectral
shape:
de= 0.0545;
SSV= 0.9962
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Modified image after simple morphological filtering.
Sky
Buildings
Vegetation
Original image
Pre-processing hyperspectral images
Morphological filteringto reduce the effect of outliers in the clustering step procedure. In addition, the reduction of non-relevant details in the images .
Methods
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Adapted Fuzzy C -means for clustering
Results
…using synthetic hyperspectral images
mean Std P95 P75
% correct pixels FCM 88,88 13,26 100,00 99,99
FCM with SSV 99,49 3,42 100,00 100,00
GFC FCM 0,9981 0,0062 1,0000 1,0000
FCM with SSV 0,9991 0,0026 1,0000 1,0000
∆ELab
FCM 0,9 1,2 3,4 1,4
FCM with SSV 0,8 1,6 4,3 0,7
RMSE FCM 0,0025 0,0028 0,0088 0,0039
FCM with SSV 0,0022 0,0049 0,0125 0,0011
Performance of the algorithms: classical FCM and the proposal FCM
with the additional SSV metric.
classical FCM FCM with SSV
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Results
Simulated RGB images of hyperspectral urban scenes
including vegetation, buildings and sky
Adapted Fuzzy C -means for clustering
…using hyperspectral urban scenes
Classical FCM results showing the most
relevant areas of the scenes.
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Simulated RGB images of hyperspectral urban scenes
including vegetation, buildings and sky.
Second row: classical FCM results.
Third row: results using the adapted FCM with SSV
metric
Results
Adapted Fuzzy C -means for clustering
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Spectral homogeneity within clusters
computing SSV between each pixel and
its representative sample
Results
FCM FCM with SSV
Image mean std P95 P90 P75 mean std P95 P90 P75
1 0,1552 0,2261 0,8801 0,3123 0,1222 0,0654 0,0616 0,1590 0,1294 0,0638
2 0,2835 0,2699 0,9324 0,7867 0,3498 0,1497 0,1569 0,4932 0,3683 0,1734
3 0,1884 0,2089 0,7012 0,5601 0,1794 0,1518 0,2393 0,8877 0,3067 0,1011
Adapted Fuzzy C -means for spectral clustering
Adapted FCM with SSV
Adapted FCM+SSV creates uniform and
compact clusters and reduces inhomogeneities
within clusters.
…using hyperspectral urban scenes
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Results
Adapted Fuzzy C -means for spectral clustering
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Results
Adapted Fuzzy C -means for spectral clustering
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Results
Adapted Fuzzy C -means for spectral clustering
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Buildings
Vegetation
Conclusions
�Bragg grating-based spectral imager to reliably
estimate spectral reflectance at a pixel.
�A modified FCM+SSV algorithm for
hyperspectral image segmentation; thus
spectral data can share some common/simple
features (e.g. vegetation, sky, etc.).
�For each pixel, highest membership degree will
allow to select appropriate labels which
combine both spectral signatures and color
characteristics.
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Raúl LuzónPh.D. student
Aida RodríguezResearcher
Juan Luis NievesAssociate Professor
Thank you for your attention!
Eva ValeroAssociate Professor
Javier RomeroProfessor
Javier HernándezAssociate Professor
Félix A. Navas
Researcher
Timo EckhardPh.D. student
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