Classification of Hyperspectral Data Using Spectral-Spatial Approaches Yuliya Tarabalka GIPSA-Lab, Grenoble Institute of Technology, France Department of Electrical and Computer Engineering, University of Iceland, Iceland Thesis committee Melba CRAWFORD President Jean-Yves TOURNERET Opponent Xiuping JIA Opponent Antonio PLAZA Opponent Paolo GAMBA Opponent Johannes R. SVEINSSON Examinator Jón Atli BENEDIKTSSON Thesis advisor Jocelyn CHANUSSOT Thesis advisor June 14, 2010
106
Embed
Classification of Hyperspectral Data Using Spectral ...yuliya.tarabalka/files/Tarabalka_DefenseFinal.pdf · ClassificationofHyperspectralDataUsing Spectral-SpatialApproaches YuliyaTarabalka
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Classification of Hyperspectral Data UsingSpectral-Spatial Approaches
Yuliya Tarabalka
GIPSA-Lab, Grenoble Institute of Technology, FranceDepartment of Electrical and Computer Engineering, University of Iceland, Iceland
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 4
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Classification approaches
Only spectral information
Pixelwise approach
Spectrum of each pixel is analyzedVariety of methods
Support Vector Machines (SVM) → goodclassification results [Camps-Valls05]
⇒
alphaltmeadowsgraveltrees
metal sheetsbare soilbitumenbricks
shadows
Overall accuracy (OA) = 81.01%
Average accuracy (AA) = 88.25%
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 5
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Classification approaches
Only spectral information
Pixelwise approach
Spectrum of each pixel is analyzedVariety of methods
Support Vector Machines (SVM) → goodclassification results [Camps-Valls05]
Spectral + spatial information
Info about spatial structures is includedSince neighboring pixels are related
How to define spatial structures?
How to combine spectral and spatialinformation?
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 6
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
1 Closest fixed neighborhoodsMarkov Random Field [Pony00, Jackson02,Farag05]Contextual features [Camps-Valls06]+ Simplicity− Imprecision at the border of regions
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 7
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
1 Closest fixed neighborhoodsMarkov Random Field [Pony00, Jackson02,Farag05]Contextual features [Camps-Valls06]+ Simplicity− Imprecision at the border of regions
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 7
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
1 Closest fixed neighborhoodsMarkov Random Field [Pony00, Jackson02,Farag05]Contextual features [Camps-Valls06]+ Simplicity− Imprecision at the border of regions
2 Morphological and area filteringMorphological profiles [Pesaresi01, Dell’Acqua04, Benediktsson05]Self-complementary area filtering [Fauvel07]+ Neighborhoods are adapted to the structures− Neighborhoods are scale dependent ⇒ imprecision in the spatial info
Closing − Original − Opening
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 7
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
1 Closest fixed neighborhoodsMarkov Random Field [Pony00, Jackson02,Farag05]Contextual features [Camps-Valls06]+ Simplicity− Imprecision at the border of regions
2 Morphological and area filteringMorphological profiles [Pesaresi01, Dell’Acqua04, Benediktsson05]Self-complementary area filtering [Fauvel07]+ Neighborhoods are adapted to the structures− Neighborhoods are scale dependent ⇒ imprecision in the spatial info
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 7
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
3 Segmentation map = exhaustive partitioning of the image intohomogeneous regions
Extraction and Classification of Homogeneous Objects [Kettig76]+ Has become a standard spectral-spatial classification technique− Statistical approach ⇒ not well adapted for hyperspectral data
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 8
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
3 Segmentation map = exhaustive partitioning of the image intohomogeneous regions
Extraction and Classification of Homogeneous Objects [Kettig76]+ Has become a standard spectral-spatial classification technique− Statistical approach ⇒ not well adapted for hyperspectral data
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 8
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
State of the art: Approaches for extracting spatial info
3 Segmentation map = exhaustive partitioning of the image intohomogeneous regions
Extraction and Classification of Homogeneous Objects [Kettig76]+ Has become a standard spectral-spatial classification technique− Statistical approach ⇒ not well adapted for hyperspectral data
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 14
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 14
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 14
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 14
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Dissimilarity criterion (DC):Spectral Angle Mapper (SAM)between the region mean vectors ui and uj
SAM(ui , uj) = arccos(ui · uj
‖ui‖2‖uj‖2)
1 Each pixel - one region2 Find DCmin between adjacent regions3 Merge adjacent regions with DC = DCmin4 Merge non-adjacent regions withDC ≤ DCmin ·SpectralClusterWeight
5 If not converge, go to 2
7575 regions
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 16
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, “Spectral-spatial classification of hyperspectralimagery based on partitional clustering techniques,” IEEE Trans. on Geoscience and Remote Sensing,vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 18
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1 Spectral-spatial classification improves accuracies when compared topixel-wise classification
2 Several segmentation techniques are investigated3 The HSEG segmentation map leads to the best classification4 Obtained classification accuracies > all previous results
However...
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 22
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using Minimum Spanning Forest grown from automatically selected markers,” IEEE Trans. onSystems, Man, and Cybernetics, Part B: Cybernetics, 2010, DOI 10.1109/TSMCB.2009.2037132.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 28
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
SVM classifier* → well suited forhyperspectral images
Output:
classification map probability map
-
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
probability estimate for each pixel tobelong to the assigned class
*C. Chang and C. Lin, "LIBSVM: A library for Support Vector Machines," Software available at
http://www.csie.ntu.edu.tw/∼cjlin/libsvm, 2001.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 29
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map HH
HY
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
Must contain a marker!
HHHHH
HHHHHH
HHHHHj
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
Must contain a marker!
HHHHH
HHHHHH
HHHHHj
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
If it is large (> 20 pixels) → use P%(5%) of its pixels with the highestprobabilities as a markerIf it is small → its pixels withprobabilities > T% (90%)are used as a marker
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
Has a marker only if it isvery reliable
HHHH
HHHHHH
HHHHHHj
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection using probabilistic SVM
Analysis of classification and probability maps:classification map probability map
Each connected component → 1 or 0 marker(2250 regions → 107 markers)
Marker is not necessarily a connected set ofpixels
Each marker has a class label
Hyperspectral image (B bands)
Probabilistic pixelwise
classification classification map
Selection of the most probability map
map of Marker-controlled
region growing markers reliable classified
pixels
Segmentation map + classification map
map of 107 markers
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 30
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Multiple classifier approach for marker selection
Previous method: strong dependence on the performances of theselected probabilistic classifier
Objective: mitigate this dependence→ using multiple classifiers
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 31
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Multiple classifier approach for marker selection
Previous method: strong dependence on the performances of theselected probabilistic classifier
Objective: mitigate this dependence→ using multiple classifiers
Multiple classifier marker selection approach
1 Classify an image by severalindependent classifiers
2 Pixels assigned by all theclassifiers to the same class
⇓Map of markers
classifiers
Hyperspectral image
Map ofmarkers
Marker selection
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 31
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection Map of markers Segmentation by EM for Gaussian mixture resolving
Hierarchical SEGmentation
Majority voting within segmentation
regions
Majority voting within segmentation
regions
classification map
classification map
classification map
Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Multiple spectral-spatialclassification approach for hyperspectral data,” submitted to IEEE Trans. on Geoscience andRemote Sensing (under review).
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 32
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection Map of markers Segmentation by EM for Gaussian mixture resolving
Hierarchical SEGmentation
Majority voting within segmentation
regions
Majority voting within segmentation
regions
classification map
classification map
classification map
Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Multiple spectral-spatialclassification approach for hyperspectral data,” submitted to IEEE Trans. on Geoscience andRemote Sensing (under review).
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 32
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection Map of markers Segmentation by EM for Gaussian mixture resolving
Hierarchical SEGmentation
Majority voting within segmentation
regions
Majority voting within segmentation
regions
classification map
classification map
classification map
Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Multiple spectral-spatialclassification approach for hyperspectral data,” submitted to IEEE Trans. on Geoscience andRemote Sensing (under review).
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 32
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection Map of markers Segmentation by EM for Gaussian mixture resolving
Hierarchical SEGmentation
Majority voting within segmentation
regions
Majority voting within segmentation
regions
classification map
classification map
classification map
Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Multiple spectral-spatialclassification approach for hyperspectral data,” submitted to IEEE Trans. on Geoscience andRemote Sensing (under review).
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 32
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Marker selection Map of markers Segmentation by EM for Gaussian mixture resolving
Hierarchical SEGmentation
Majority voting within segmentation
regions
Majority voting within segmentation
regions
classification map
classification map
classification map
Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Multiple spectral-spatialclassification approach for hyperspectral data,” submitted to IEEE Trans. on Geoscience andRemote Sensing (under review).
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 32
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Outline
1 Introduction
2 Classification using segmentation-derived adaptive neighborhoodsSegmentationSpectral-spatial classificationConcluding discussion
3 Segmentation and classification using automatically selected markersMarker selection
Using probabilistic SVMMultiple classifier approach
MSF-based marker-controlled region growingConcluding discussion
4 Conclusions and perspectives
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 33
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
Hyperspectral image (B bands)
Classification
map of Construction of minimum spanning
forest
markers Marker selection
Majority voting within connected
components
Segmentation map + classification map
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using Minimum Spanning Forest grown from automatically selected markers,” IEEE Trans. onSystems, Man, and Cybernetics, Part B: Cybernetics, 2010, DOI 10.1109/TSMCB.2009.2037132.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 34
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
B bands x 9x 8x 7
x 6x 5x x1 x2 x3
4
map of markers
1 1 0 0 0 0 0 0 2
1) Map an image onto a graphWeight wi ,j indicates the degree of dissimilarity between pixels xi and xj .Spectral Angle Mapper (SAM) distance can be used:
wi ,j = SAM(xi , xj) = arccos
( ∑Bb=1 xibxjb
[∑B
b=1 x2ib]1/2[∑B
b=1 x2jb]1/2
)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 35
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
B bands x 9x 8x 7
x 6x 5x x1 x2 x3
4
map of markers
1 1 0 0 0 0 0 0 2 image graph
1) Map an image onto a graphWeight wi ,j indicates the degree of dissimilarity between pixels xi and xj .Spectral Angle Mapper (SAM) distance can be used:
wi ,j = SAM(xi , xj) = arccos
( ∑Bb=1 xibxjb
[∑B
b=1 x2ib]1/2[∑B
b=1 x2jb]1/2
)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 35
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
B bands x 9x 8x 7
x 6x 5x 4
x3 x2 x1
map of markers
1 1 0 0 0 0 0 0 2
w12
image graph
1) Map an image onto a graphWeight wi ,j indicates the degree of dissimilarity between pixels xi and xj .Spectral Angle Mapper (SAM) distance can be used:
wi ,j = SAM(xi , xj) = arccos
( ∑Bb=1 xibxjb
[∑B
b=1 x2ib]1/2[∑B
b=1 x2jb]1/2
)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 35
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
B bands x 9x 8x 7
x 6x 5x 4
x3 x2 x1
map of markers
1 1 0 0 0 0 0 0 2 image graph
0 8 14 2
8 5 15
9 5164
1211 12 314 10
511
8
1) Map an image onto a graphWeight wi ,j indicates the degree of dissimilarity between pixels xi and xj .Spectral Angle Mapper (SAM) distance can be used:
wi ,j = SAM(xi , xj) = arccos
( ∑Bb=1 xibxjb
[∑B
b=1 x2ib]1/2[∑B
b=1 x2jb]1/2
)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 35
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
1 1
2
B bands x 9x 8x 7
x 6x 5x 4
x3 x2 x1
map of markers
1 1 0 0 0 0 0 0 2
80 0 14 2
8 5 15
image graph
0 0 9 5
164 0 1211
12 314 10 5
11 80 0
1) Map an image onto a graphWeight wi ,j indicates the degree of dissimilarity between pixels xi and xj .Spectral Angle Mapper (SAM) distance can be used:
wi ,j = SAM(xi , xj) = arccos
( ∑Bb=1 xibxjb
[∑B
b=1 x2ib]1/2[∑B
b=1 x2jb]1/2
)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 35
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
80
1 1
2
02 14
5 8 15 9
image graph
5164 0 0 0
11 12 14 12 3 10 11
8 5 0 0
Given a graph G, a MSF F ∗ rooted on vertices {r1, ..., rm} is:a non-connected graph without cycles
contains all the vertices of G
consists of connected subgraphs, each subgraph (tree) contains (isrooted on) one root risum of the edges weights of F ∗ is minimal
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 36
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
modified graph
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
2) Add m extra vertices ri , i = 1, ..., m corresponding to m markers
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 0
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 1
0
0
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
0 1
0
2
0 0
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
1 1
1
2
2 2
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
Initialization: V ∗ = {r1, r2, ..., rm} (roots are in the forest)1 Choose edge of the modified graph ei j with minimal weight such thati ∈ V ∗ and j /∈ V ∗
2 V ∗ = V ∗ ∪ {j}, E∗ = E∗ ∪ {ei ,j}3 If V ∗ 6= V , go to 1
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
0
1 1
2
0
1 1
1
2
2 2
3
8
164
8 5
5
14
5
2
9
14
10 1111 12
8
12
15
r2
r1 0
0
3) Construct a MSF F ∗ = (V ∗, E∗)
4) Class of each marker → class of the corresponding region(of all the pixels grown from this marker)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 37
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
Pixelwiseclassification map
⇒
Map of107 markers
⇒
MSF-basedclassification map
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 38
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Construction of a Minimum Spanning Forest (MSF)
Pixelwiseclassification map
⇒
Map of107 markers
⇒
MSF-basedclassification map
If a markeris classified
to the wrong class⇒
The whole region grownfrom this marker
risks to bewrongly classified!
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 38
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 42
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
Outline
1 Introduction
2 Classification using segmentation-derived adaptive neighborhoodsSegmentationSpectral-spatial classificationConcluding discussion
3 Segmentation and classification using automatically selected markersMarker selection
Using probabilistic SVMMultiple classifier approach
MSF-based marker-controlled region growingConcluding discussion
4 Conclusions and perspectives
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 43
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Marker selectionMSF-based marker-controlled region growingConcluding discussion
1 Classification using automatically selected markers:significantly decreases oversegmentationimproves classification accuraciesprovides classification maps with homogeneous regions
2 Marker selection: it is advantageous to useSVM classifierspatial informationmultiple classifier approaches
3 Marker-controlled region growingMSF-based method has proven to be efficient and robust
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 44
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Contributions
Three spectral-spatial classification strategies:1 Using SVM and MRF models
2 Using adaptive neighborhoods derived from segmentationSegmentation techniques working both in the spatial and spectral domain→ good performancesPixelwise classification + majority voting within regions→ simple and efficient technique
3 Using marker-based region growing segmentationAnalyzing probabilistic classification results for marker selectionInterest of using spatial information and multiple classifier approachesfor marker selectionMSF-based marker-controlled region growing→ efficient and robust
Possibilities of high-performance parallel computing using commodityprocessors (GPUs)
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 45
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Perspectives
1 Spectral-spatial image analysisAutomatically select results in segmentation hierarchiesDevelop new similarity measuresPerform segmentation and classification concurrently
2 Further explore parallel strategies using commodity processors
3 Apply and adapt the proposed methods for other types ofdata/applications
medical imaging
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 46
Classification of Hyperspectral Data UsingSpectral-Spatial Approaches
Yuliya Tarabalka
GIPSA-Lab, Grenoble Institute of Technology, FranceDepartment of Electrical and Computer Engineering, University of Iceland, Iceland
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1. Watershed segmentation for hyperspectral image
From B-band image →1-band segmentation map:
Feature extraction (PCA,ICA,...)?
Vector gradient?
Combine B gradients?
Combine B watershed regions?
Gradient (1 band – 1 band)
Watershed
Hyperspectral image (B bands)
Feature extraction (B bands – 1 band)
Combine gradients (B bands – 1 band)
(1 band – 1 band)
Segmentation map (1 band)
Combine regions (B bands – 1 band)
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 48
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1. Watershed segmentation for hyperspectral image
From B-band image →1-band segmentation map:
Feature extraction (PCA,ICA,...)?
Vector gradient?
Combine B gradients?
Combine B watershed regions?
Gradient (1 band – 1 band)
Watershed
Hyperspectral image (B bands)
Feature extraction (B bands – 1 band)
Combine gradients (B bands – 1 band)
(1 band – 1 band)
Segmentation map (1 band)
Combine regions (B bands – 1 band)
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 48
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1. Watershed segmentation for hyperspectral image
From B-band image →1-band segmentation map:
Feature extraction (PCA,ICA,...)?
Vector gradient?
Combine B gradients?
Combine B watershed regions?
Gradient (B bands – 1 band)
Watershed
Hyperspectral image (B bands)
Feature extraction (B bands – 1 band)
Combine gradients (B bands – 1 band)
(1 band – 1 band)
Segmentation map (1 band)
Combine regions (B bands – 1 band)
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 48
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1. Watershed segmentation for hyperspectral image
From B-band image →1-band segmentation map:
Feature extraction (PCA,ICA,...)?
Vector gradient?
Combine B gradients?
Combine B watershed regions?
Gradient (B bands – B bands)
Watershed
Hyperspectral image (B bands)
Feature extraction (B bands – 1 band)
Combine gradients (B bands – 1 band)
(1 band – 1 band)
Segmentation map (1 band)
Combine regions (B bands – 1 band)
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 48
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
1. Watershed segmentation for hyperspectral image
From B-band image →1-band segmentation map:
Feature extraction (PCA,ICA,...)?
Vector gradient?
Combine B gradients?
Combine B watershed regions?
Gradient (B bands – B bands)
Watershed
Hyperspectral image (B bands)
Feature extraction (B bands – 1 band)
Combine gradients
(B bands – B bands)
Segmentation map (1 band)
Combine regions (B bands – 1 band)
Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectralimages using watershed transformation,” Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July2010.
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 48
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Gradient
Robust Color Morphological Gradient (RCMG):
For each pixel xp, χ = [x1p, x2p, ..., xep] is aset of e vectors within E
Color Morphological Gradient (CMG):
CMGE(xp) = maxi ,j∈χ{‖xip − xjp‖2}
RCMG:xi_maxp , xj_maxp - pixels that define theCMG of xp
RCMGE(xp) = maxi ,j∈[χ−[xi_maxp ,xj_maxp ]]
{‖xip−xjp‖2}
Hyperspectral image X ( B bands)
Gradient
Feature extraction
Combine gradients
Watershed
Spectro-spatial classification
Spectral information
E
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 49
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Gradient
Robust Color Morphological Gradient (RCMG):
For each pixel xp, χ = [x1p, x2p, ..., xep] is aset of e vectors within E
Color Morphological Gradient (CMG):
CMGE(xp) = maxi ,j∈χ{‖xip − xjp‖2}
RCMG:xi_maxp , xj_maxp - pixels that define theCMG of xp
RCMGE(xp) = maxi ,j∈[χ−[xi_maxp ,xj_maxp ]]
{‖xip−xjp‖2}
Hyperspectral image X ( B bands)
Gradient
Feature extraction
Combine gradients
Watershed
Spectro-spatial classification
Spectral information
E
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 49
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Gradient
Robust Color Morphological Gradient (RCMG):
For each pixel xp, χ = [x1p, x2p, ..., xep] is aset of e vectors within E
Color Morphological Gradient (CMG):
CMGE(xp) = maxi ,j∈χ{‖xip − xjp‖2}
RCMG:xi_maxp , xj_maxp - pixels that define theCMG of xp
RCMGE(xp) = maxi ,j∈[χ−[xi_maxp ,xj_maxp ]]
{‖xip−xjp‖2}
Hyperspectral image X ( B bands)
Gradient
Feature extraction
Combine gradients
Watershed
Spectro-spatial classification
Spectral information
E
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 49
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
MC-MSF classification
3-NN
∧
ML
∧
SVM
=
Map of MC markers
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 50
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
MC-MSF classification
3-NN
∧
ML
∧
SVM
=
MC-MSF clas. map
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 50
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
MSSC-MSF classification
WH+MV
∧
EM+MV
∧
RHSEG+MV
=
Map of markers
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 51
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
MSSC-MSF classification
WH+MV
∧
EM+MV
∧
RHSEG+MV
=
MSSC-MSF clas. map
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 51
IntroductionClassification using segmentation-derived adaptive neighborhoods
Segmentation and classification using automatically selected markersConclusions and perspectives
Segmentation and classification of the ROSIS image
SVM classif map13648 regions
RHSEG segm map7575 regions
RHSEG+MV clas map1863 regions
MSSC clas map802 regions
Yuliya Tarabalka ([email protected]) Spectral-Spatial Classification of Hyperspectral Data 52