compact polarimetric sar classification in urban area...
TRANSCRIPT
Lu Xu1,2, Hong Zhang1,*, Chao Wang1
Compact Polarimetric SAR Classification in Urban Area
with Multi-Feature Using Extreme Learning Machine
1 Key Laboratory of Digital Earth Science, Institute of Remote
Sensing and Digital Earth
2 University of Chinese Academy of Sciences, Beijing,
100049, China
E-mail: [email protected]
01
02
03
04
Introduction
Methodology
Experiments and Discussions
Conclusions
Outlines
IntroductionCompact polarimetric(CP) SAR has advantageous implications concerning
system design and implementation issues:
Reduced hardware complexity;
Larger swaths;
Transmit power halved with respect to that of a quad-polarimetric system.
Future CP Projects:
Canada: Radarsat Constellation Mission
(S.R. Cloude, 2012);
America: DESDynI (Charbonneau,2010);
Japan: Advanced Land Observing
Satellite-2 (S.R. Cloude, 2012).
Though CP SAR is not alternatives for fully polarimetric (FP) SAR,
the larger swath make it suitable for land observation.
Current systems:
ESA: Chandrayaan-1 (Raney et al., 2011);
India: Risat-1 (Chakraborty et al., 2013);
IntroductionCompact polarimetric simulation：
4 : 2
: 2 2
: 2
T
hh hv vv hv
T
hh vv hv hh vv
T
hh hv vv hv
k S S S S
DCP k S S i S i S S
CTLR k S iS iS S
Compact data could be simulated
from quad-pol data according to
the linear relationship of their
scattering vector elements.
V
H
V
H
DCP modeπ/4 mode CTLR mode
Transmit Transmit
Transmit
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Introduction
• The potential of CP SAR in land classification has been explored by several
preceding researches.
Classification with reconstructed pseudo
full-polarimetric data:
Souyris et al., 2005: the potential of
pseudo full-polarimetric (FP) covariance
matrix in classification.
Ainsworth et al., 2009: the reconstructed
FP achieves similar crop classification
precision compared with the simulated
CTLR and π/4 mode images with
Wishart classifier .
Classification with polarimetric features
of CP SAR:
Chen et al., 2009: unsupervised Wishart
classification based on SPAN and m-δ
decomposition
Lardeux et al., 2010: multi-feature SVM
classification comparison on CP SAR,
dual-pol and quad-pol images in tropical
forest.
Guo et al., 2015: Wishart classification of
DCP mode based on H/α decomposition
These researches mainly concentrate on crop and vegetation classification.
Introduction
The precise urban land classification is quite difficult because of the
blended and disorderly distribution of buildings, plants, bare solid and
so on.
Multi-feature classification strategy are preferred since urban regions
contain numerous complex and hybrid land objects. We believe that
various of polarimetric information should be helpful for discriminating
different land covers.
The Extreme Learning Machine (ELM) is adopted for its fast processing ability
to integrate different polarimetric features and achieve the classification.
We want to see how CP SAR performs on urban land classification.
01
02
03
04
Introduction
Methodology
Experiments and Discussions
Conclusions
Outlines
Methodology ELM was proposed by Huang et al. for single-hidden layer feed-forward
neural networks (SLFNs) , which overcomes the shortage of traditional feed-
forward neural networks where all parameters of the network need to be
iteratively calculated and local minima might occur
For SLFNs, the main aim of training process is to obtain network parameters that
minimize error function defined by [1]:
1
N
1
i
n
x1
x2
y1
β1
βi
βn
[1] Mulyono, S., T. Pianto, M. I. Fanany, and T. Basaruddin (2013), “An ensemble incremental approach of Extreme Learning Machine
(ELM) For paddy growth stages classification using MODIS remote sensing images”, 2013 International Conference onAdvanced Computer
Science and Information Systems (ICACSIS), IEEE.
MethodologyFor a SLFN with n additive hidden nodes, the decision function is expressed as:
1
( ) ( )
,
;n
i i i
i
n
i i i
N p
f g w b
R w R R b R
x x
x ， ， ，
g(x) : the activation function; x: input sample vector with N elements;
bi : bias of the ith hidden node; wi: weight vector from input to hidden layer;
P: the number of output nodes. βi : weight vector from the hidden layer to the output layer;
Matrix form:
N P N n n PF G
1 1 1 1
1 1 2 2
1 1
, , , ,
, , , ,
, , , ,
n n
n n
N n n N N n
g w b x g w b x
g w b x g w b xG
g w b x g w b x
1
† T TG F G G G
where G† is the Moore-Penrose
generalized inverse of matrix G.
The weight matrix β could be solved
according to the minimum norm least
square function:
1 2
1 2
, , ,
, , ,
;i i i iN
i i i iP
w w w w
Methodology
The procedure of ELM method could be summarized as follow:
1) input the training samples x and hidden nodes number n, set the activation
function g(x);
2) select the input layer weight W and the bias B randomly;
3) calculate the matrix G;
4) calculate the output weight matrix β.
The final output contains the layer weight W, the bias B and the output weight β.
The ELM classifier
For an area of m-class, the output node number should be set as m.
If the ith node is the maximum among all m nodes, then the input sample belongs
to class i.
MethodologySeventeen features potential in portraying scattering mechanisms are selected from
the simulated CTLR image as polarimetric indicators for subsequent classification:
covariance matrix elements C11,C22,C12
two eigenvalues l1,l2
H/α/A decomposition entropy H, scattering angle α,
anisotropy A
m-χ decomposition
RVOG based three-component
decomposition
SPAN span
Contrast
Shannon entropy SE=SEI+SEP
0
0
0
1 cos 2 2
1
1 cos 2 2
D s
V
S s
P g m
P g m
P g m
0
0
0
1 sin 2 2
1
1 sin 2 2
Pd g m
Pv g m
Ps g m
1 0contrast g g
Methodology
Post-processing: Vote Strategy
To maintain the consistency of small land parcel and reduce
randomness of noises, a frequently used process is majority voting
algorithm.
We apply segmentation to SPAN image, on which energy-consistent
objects could be acquired based on edge extraction.
(The segmentation algorithm is provided by ENVI 5.1.)
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Introduction
Methodology
Experiments and Discussions
Conclusions
Outlines
Experiments and Discussions
Information of test area
Location: Suzhou city, Jiangsu Province,
China.
Mode: Radarsat-2 Fine Quad mode image.
Data: 29th August, 2014.
Incidence angle: 38.37°~ 39.84°
Pixel spacing: 4.73m×4.74m
(azimuth×range).
Area size: 900×600 pixels
Pauli image of study area
The right-circular CTLR image is
simulated accordingly.
Experiments and Discussions
Ground truth of study area
Water
Dense buildings
Lush vegetation
Thin vegetation
Sparse buildings
Class name Total number
Water 89205
Dense buildings 139810
Lush vegetation 61346
Thin vegetation 145677
Sparse buildings 103962
Dense
buildings
Sparse
buildings
Experiments and Discussions
Google earth image of study area
Experiments and Discussions
m-χ decomposition image of CTLR image
1) Apply a 3×3 refined Lee filter
2) Calculate the selected polarimetric
features pixel by pixel and normalized
into range [0, 1].
3) Parameters of ELM:
the active function: sigmoid function
the number of hidden nodes: 100.
Class name Sample number
Water 1033
Dense buildings 1234
Lush vegetation 1404
Thin vegetation 1106
Sparse buildings 1071
Experiments and Discussions
multi-feature method with ELM classifier
(pixel-based)Wishart classifier
(pixel-based)
Experiments and DiscussionsPost-processing: Vote Strategy
Get the objects through segmentation
to span image
Overlay object edges on pixel-based
classification result
Majority voting
to improve the
result.
Experiments and Discussions
multi-feature method with ELM classifier
(object-based)
Wishart classifier
(object-based)
Two main difficulties in urban classification:
1. The mix-up of artificial growing plants and residential buildings: the well-
organized arrangements bring homogeneous textures for dense buildings
which are similar to vegetation.
2. The mixed implantation of plants: misclassification of different vegetation.
Experiments and Discussions
These problems are intrinsically caused by imaging principle of SAR
system, and are difficult to improve with single data set. As a result, the
accuracy of urban classification would not be high. However, the main
distributions of different classes have been accurately described.
Experiments and Discussions
Method ELM Wishart
Accuracy(%) Prod. User. Prod. User.
Water 86.79 88.61 89.80 87.81
Dense buildings 48.12 25.07 39.14 31.24
Lush vegetation 44.62 69.96 48.35 39.19
Thin vegetation 50.62 48.93 35.20 67.08
Sparse buildings 62.35 52.56 61.51 41.63
Overall 55.93 50.67
Analyzations on accuracies:
The ELM classifier largely reduces false alarms occurred in dense
buildings and thin vegetation. (Producer’s accuracy)
Besides, detection rates for lush vegetation and sparse buildings are
also increased. (User’s accuracy)
Although the User’s accuracy for dense buildings and thin vegetation are slightly lower
than Wishart classifier, the accuracies of lush vegetation and sparse buildings are largely
improved, and the overall accuracy of ELM classifier is increased.
01
02
03
04
Introduction
Methodology
Experiments and Discussions
Conclusions
Outlines
Compared with Wishart classifier, ELM classifier displays improvements in
discriminating lush vegetation and strong, sparse buildings. (higher User’s
accuracy)
ELM classifier could not reduce the misclassification between dense
buildings and lush vegetation, but the false alarm are lower. (higher
Producer’s accuracy)
These improvements leads to a better overall accuracy.
Conclusions
A CP SAR classification assesment for urban area is carried out.
ELM classifier is adopted with seventeen polarimetric features to
acomplish the classification.
An object-oriented voting strategy is applied according to edge detection
on span image for post-processing.
Future improvements
Comparison with more classifier, including SVM, random forest and so
on.
More features, including texture information.
Detailed analysis about urban buildings with specific field research.
Better segmentation method, such as SLIC or N-cut algorithm.
Thank you！