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)
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.
… 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…
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
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
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
Calibrated dispersive devices
Typicalspectral
measurement configurations
Limited FOV
400 500 600 7000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Wavelength (nm)
Spectral image acquisition
� Better resolution than conventional spectroradiometers
� Easy and cheaper
Spectral image acquisition
)(λE
400 500 600 7000
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Wavelength (nm)
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
Spectral image acquisition
Spectral line cameras
Different approaches
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
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
( ) ( )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…
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
� 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
Hyperspectral image dataset
Real spectral images: urban scenes
Methods
False-color synthetic hyperspectral images.
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
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:
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
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
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
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.
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
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
Results
Adapted Fuzzy C -means for spectral clustering
Results
Adapted Fuzzy C -means for spectral clustering
Results
Adapted Fuzzy C -means for spectral clustering
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.
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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