Classification Using Extended Morphological
Attribute Profiles Based On Different Feature
Extraction Techniques
Stiijn Peeters
University of Antwerp, Antwerp, Belgium
Dr. Prashanth Marpu
Mattia Pedergnana
Prof. Jon Atli Benediktsson
University of Iceland, Reykjavik, Iceland
Dr. Mauro Dalla Mura
Prof. Lorenzo Bruzzone
University of Trento, Trento, Italy
1
Overview





Background
Morphological Attribute Profiles
Classification of Hyperspectral data
Ongoing work
Conclusions
2
Background



Morphological profiles (MP) and Morphological attribute profiles (MAP)
have been successfully used to fuse spectral and spatial information for
the classification of remote sensing data.
J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of Hyperspectral Data From Urban
Areas Based on Extended Morphological Profiles,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp.
480–490, Mar. 2005
M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, ”Morphological Attribute Profiles for the
Analysis of Very High Resolution Images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747–
3762, Oct. 2010
3
Background


Traditionally, MPs and MAPs are built using the feature extraction
based on principal component analysis (PCA).
Moreover, the selection of filter parameters is traditionally done
manually.
In this study,
1.
We analyse the classification results by using various feature
extraction techniques (PCA, Kernel PCA, DAFE, DBFE).
2.
We use a simple method to build the MAPs based on standard
deviation attribute automatically.
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Morphological Profile (MP)
and
Morphological Attribute Profile (MAP)
5
Morphological Profiles
When dealing with real images it is difficult to identify a
single filter parameter suitable to handle all the objects in
the image.
Perform a multilevel analysis by using several values for
the filter parameters. Build a stack of images with different
degrees of filtering.
Morphological Profile (MP)
M. Pesaresi and J. A. Benediktsson, “A new approach for the morphological segmentation of high-resolution satellite imagery,"
IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 2, pp. 309-320, 2001.
6
6
Morphological Profiles
Morphological Profiles (MPs) are composed by a sequence of opening and closing with SE
of increasing size.
Closing Profile
Opening Profile
MP
Square SE
Sizes: 7, 13, 19, 25
7
7
Extended Morphological Profile
Morphological
profile 1
F1
X
Feature
Reduction
F2
MP
MP
MP
XX111
MP
XX121
Hyperspectral
Image
MP
XX121
X
MP
MP
EMP
X1112
MP
EMP
XX111
Extended
Morphological
Profile
Fn
MP
XX1n1
MP
Morphological
profile n
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Attribute Profiles
Thickening Profile
Thinning Profile
Square SE (MP)
Sizes: 7, 13, 19
Area Attribute
λ: 45, 169, 361
Criterion: Area > λ
Moment of Inertia
Attribute
λ: 0.2, 0.1, 0.3
Criterion: Inertia > λ
STD Attribute
λ: 10, 20, 30
Criterion: STD > λ
9
Selection of thresholds for constructing MAP
In this study, we only use the attribute profile generated using the standard deviation
attribute.
The thresholds to build the profile are estimated for every feature separately from the
range of standard deviation values of the training samples of all the classes. So,
different threshold values are used for diferent profiles.
A more general approach to use a big range of attributes has been recently proposed.
An entire profile using a wide range of attributes and wide range of thresholds is built
and a newly proposed hybrid genetic algorithm is used for feature selection.
Master Thesis: Mattia Pedergnana (University of Iceland, Iceland and University of
Trento, Italy)
Optimal Automatic Construction of Morphological Profiles
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Results
11
Data used
ROSIS University of Pavia
12
Data used
AVIRIS Indian Pine
13
Data used

ROSIS University of Pavia
AVIRIS Indian Pines
Training
Test
Corn-notill
50
1384
Corn-mintill
50
784
Corn
50
184
Grass-pasture
50
447
1815
Grass-trees
50
697
265
1113
Hay-windrowed
50
439
Shadow
231
795
Soybean-notill
50
918
Bricks
514
3364
Soybean-mintill
50
2418
Meadows
540
18146
Soybean-clean
50
564
Bare soil
532
4572
Wheat
50
162
Woods
50
1244
Bld-Grass-Trees-D
50
330
Stone-Steel-Towers
50
45
Alfalfa
15
39
Grass-pasture-mowed
15
11
Oats
15
5
Training
Test
Trees
524
2912
Asphalt
548
6304
Bitumen
375
981
Gravel
392
Metal sheets
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Results: University of Pavia data
PCA
OA
AA
k
SVM
92.01%
92.17%
0.8957
Random
Forest
OA
91.31%
AA
87.96%
k
0.8894
15
Results: University of Pavia data
Kernel PCA
OA
AA
k
OA
AA
k
RF
92.2%
95.02%
0.8993
SVM
92.31%
93.96%
0.9002
16
Results: University of Pavia data
DAFE
OA
AA
k
OA
AA
k
RF
96.25%
96.28%
0.951
SVM
92.69%
93.27%
0.9119
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Results: University of Pavia data
DBFE
OA
AA
k
OA
AA
k
RF
95.09%
95.32%
0.9386
SVM
93.45%
94.16%
0.9145
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Summary of Results
University of Pavia
Extended Attribute Profile using Standard deviation
RF
SVM
PCA
KPCA
DAFE
DBFE
PCA
KPCA
DAFE
DBFE
OA%
91.31
92.2
96.28
95.32
92.01
92.31
93.27
93.45
AA%
87.96
95.02
96.25
95.09
92.17
93.96
92.69
94.16
k
0.886
0.899
0.951
0.939
0.896
0.90
0.912
0.914
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Extended Morphological Profiles
PCA
RF
DAFE
RF
DBFE
RF
KPCA
RF
PCA
SVM
DAFE
SVM
DBFE
SVM
KPCA
SVM
AA (%)
90.89
95.12
96.75
93.87
91.37
92.32
94.92
94.74
OA (%)
84.75
94.88
96.107
87.89
86.92
88.93
90.00
91.47
k
0.8048
0.9324
0.9487
0.844
0.8326
0.8579
0.8720
0.8898
Params:
Initial Size: 1
Step: 2
Number Of Opening/Closing: 3
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Results: Indian Pine
PCA
OA
AA
k
OA
AA
k
RF
92.20%
95.32%
0. 9100
SVM
88.94%
92.96%
0.8738
21
Results: Indian Pine
Kernel PCA
OA
AA
k
OA
AA
k
RF
92.87%
96.01%
0.9183
SVM
88.93%
93.36%
0.8737
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Results: Indian Pine
DAFE
OA
AA
k
OA
AA
k
RF
77.21 %
87.62 %
0.7427
SVM
68.70%
71.31%
0.6464
23
Results: Indian Pine
DBFE
OA
AA
k
OA
AA
k
RF
81.28%
87.78%
0.7866
SVM
73.13%
79.81%
0.6954
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Summary of Results
Indian Pine
Extended Attribute Profile using Standard deviation
RF
SVM
PCA
KPCA
DAFE
DBFE
PCA
KPCA
DAFE
DBFE
OA%
92.20
92.87
77.21
81.28
88.94
88.93
68.70
73.13
AA%
95.32
96.01
87.62
87.78
92.96
93.36
71.31
79.81
k
0.910
0.918
0.743
0.787
0.874
0.8737
0.646
0.695
25
Extended Morphological Profiles
PCA
RF
DAFE
RF
DBFE
RF
KPCA
RF
PCA
SVM
DAFE
SVM
DBFE
SVM
KPCA
SVM
AA (%)
93.54
87.67
86.69
95.66
92.90
69.84
78.93
92.702
OA (%)
87.75
79.08
78.98
92.01
87.15
66.61
71.36
86.67
k
0.8607
0.7627
0.7615
0.9085
0.8533
0.6231
0.6763
0.8483
Params:
Initial Size: 1
Step: 2
Number Of Opening/Closing: 3
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Conclusion
The results of classifying hyperspectral data using morphological
attribute filters with various feature extraction techniques has been
studied.
Better results are obtained with attribute profiles compared to
morphological profiles.
Supervised feature reduction techniques are constrained by the number
of available samples and hence do not provide consistent results.
Linear unsupervised feature reduction techniques such as PCA may not
be useful as the features are not always able to distinguish between
classes effectively. This is observed in the experiments.
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Conclusion
 However, non-linear techniques such as kernel PCA preserve the information of
the independent clusters and hence can be useful in distinguishing between
classes. Experiments suggest that EAP with KPCA produces consistent and high
quality classification results.
 We are currently investigating the results with a wide range of feature
extraction techniques.
 We are also working on methods to automatically identify the thresholds to
build profiles.
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Experimental Results

Pavia Dataset – Second Approach – HML-DBFE-RF
Overall
Accuracy:
98.3%
Average
Accuracy:
98.6%
29
29
Experimental Results

Pavia Dataset – Second Approach – HML-KPCA-RF
Overall
Accuracy:
90%
Average
Accuracy:
97 %
30
30
Experimental Results

Pavia Dataset – Second Approach – HML-KPCA-SVM
Overall
Accuracy:
93.8%
Average
Accuracy:
96.9 %
31
31
Experimental Results
Indian Pine– Second Approach – HML-KPCA-RF
Overall
Accuracy:
93.8%
Average
Accuracy:
96 %
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Software in Matlab can be freely obtained by sending us an email.
Prashanth Marpu prashanthmarpu@ieee.org
Mattia Pedergnana mattia@mett.it
Mauro Dalla Mura dallamura@disi.unitn.it
Thank You for your attention
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