Segmentation of Dual-Frequency Polarimetric SAR Data for an Improved Land Cover
Classification
Ken Yoong LEE and Timo Rolf BRETSCHNEIDER
EADS Innovation Works Singapore
110 Seletar Aerospace View, Singapore 797562
Tel: +65 65927324; Fax: +65 66591276
E-mail: {ken-yoong.lee, timo.bretschneider}@eads.net
Abstract
In this paper an existing hybrid segmentation algorithm is extended for segmenting multi-look
polarimetric synthetic aperture radar data. There are four modules in this extended hybrid
segmentation algorithm, namely 1) speckle suppression by a spatially adaptive filter, 2) edge
magnitude computation based on Roy’s largest eigenvalue, 3) initial segmentation by
morphological watershed transform, and 4) region merging. The performance of the extended
hybrid segmentation algorithm was examined using NASA/JPL AIRSAR POLSAR data. The
obtained segmentation outputs were then used as the inputs for land cover classification. The
supervised classification was performed on per-pixel and per-segment bases using a complex
Wishart classifier. The targeted land cover classes included bareland, bridge, built-up, mangrove
forest, marshland, oil palm, rice paddy, rubber, scrub-grassland, water body, diversified cropland,
dryland forest, fish farm, mudflat as well as road. From the obtained results, the overall accuracies
were 63% and 67% for both the C- and L-band, respectively. An improved accuracy of 75% was
attained by using the dual-frequency combination. Furthermore, the per-segment approach was
found to improve the accuracy with reduced salt-and-pepper effects in the classification outputs.
Key Words: polarimetric synthetic aperture radar, segmentation, classification, land cover
1. Introduction
With the realisation of multi-frequency fully polarimetric synthetic aperture radar (POLSAR)
imaging systems, such as SIR-C, AIRSAR, E-SAR, EMISAR and Pi-SAR, it is now possible to
capture complete polarimetric signatures of the Earth’s surface. To date, there is a growing
interest within the remote sensing community to utilise the acquired data for various applications
(Boerner et al., 1998). One of the key applications is land cover classification. Multi-frequency
POLSAR data can, through different frequency and polarisation combinations, provide abundant
and valuable information (e.g. intensity, total power, polarisation ratio, phase difference and
correlation coefficient), which help in understanding and quantifying physical interaction
behaviours between radar waves and illuminated land cover features. However, POLSAR data
suffer inherently from speckle noise disturbance, which can degrade the application for land cover
classification. Being a critical pre-processing task, speckle suppression of POLSAR data remains
challenging. It is difficult for a speckle filter to differentiate perfectly noise and image features,
such as edges, lines, point targets etc., which are to be preserved. Moreover, this presence of
speckle noise can restrict the applicability of conventional image processing techniques. In
particular, the conventional edge detectors, region-based segmentation algorithms as well as
pattern recognition classifiers need to be refined by taking both speckle noise characteristics and
POLSAR data contents into account. Apart from this, most of the proposed classifiers for
POLSAR data operate on a per-pixel basis. The classification outputs based on individual pixels,
however, can be unsatisfactory due to the presence of inherent speckle noise and the exclusion of
intrinsic spatial neighbourhood information. Hence, a per-segment approach is more preferable
for POLSAR image classification. Keeping these considerations in mind, a selected hybrid
segmentation algorithm is extended in this paper for POLSAR data, followed by a quantitative
evaluation of the per-segment approach in improving land cover classification.
This paper is organised as follows: Section 2 presents the extended hybrid segmentation
algorithm. In Section 3, the segmentation and classification of POLSAR data are discussed.
Concluding remarks are given in Section 4.
2. Extended Hybrid Segmentation Algorithm
A hybrid segmentation algorithm was proposed by Haris et al. (1998) for segmenting optical
pictures. Its advantage is that both edge and region information is considered during the
segmentation process. There are four main modules in this segmentation algorithm: 1) noise
reduction, 2) gradient computation, 3) initial segmentation by morphological watershed transform,
and 4) region merging. In this study the hybrid segmentation algorithm was extended for
segmenting multi-look POLSAR data, where the four modules become 1) speckle suppression, 2)
edge magnitude computation, 3) initial segmentation by morphological watershed transform, and
4) region merging. Each module is executed consecutively and discussed in more detail below.
2.1 Speckle Suppression
POLSAR data are inherently corrupted by speckle noise. This presence of speckle noise can
significantly degrade the performance of image segmentation. Thus, a spatially adaptive speckle
filter, which was proposed by Lee and Bretschneider (2007), is employed for speckle suppression.
The proposed filter consists of five processing steps, namely 1) edge detection, 2) line detection,
3) texture analysis, 4) classification based on scattering mechanisms, and 5) despeckling. The
obtained filtering output is then used as an input for computing edge magnitudes.
2.2 Edge Magnitude Computation
The purpose of this module is to generate an edge magnitude output, which is then used as an input
into the subsequent watershed transformation. In this module, the Roy’s largest eigenvalue-based
edge detector (Lee, 2009, Chapter 4) is employed to compute edge magnitude from the filtered
POLSAR data. The corresponding processing steps are outlined in Lee (2009, p. 43). The edge
magnitude of each pixel, i.e. the maximum Roy’s largest eigenvalue, is computed by using four
basic edge templates of 33 pixels as shown in Figure 1.
2.3 Initial Segmentation by Watershed Transform
The concept of watershed was first applied by Beucher and Lantuejoul (1979) to image
segmentation problems. To date, the use of the morphological watershed transform for
segmenting single-frequency single-polarisation SAR data is well recognised and can be found in
Fjørtoft et al. (1998), Lemarechal et al. (1998), Li et al. (1999) and others. Its advantages include
that: (i) it is a natural segmentation approach, (ii) it is independent of any forms of probability
density function in the segmentation process, and (iii) it does not need to state any controls.
However, the drawback is that in most cases it produces an over-segmented result.
The watershed transform contains two steps: sorting and flooding. The input edge magnitude
image is considered as a topographic surface. Each pixel value becomes the altitude of the surface.
Firstly, all pixels are sorted in increasing order according to their edge magnitude value. The
flooding is then carried out based on an immersion simulation. There are two rules in the flooding
process: (i) random access to any pixels in the image and (ii) direct access to the neighbours of a
given pixel. The water first rises from the minimum magnitude pixels, which are also known as
local minima. The flooding process is continued for each magnitude k until the maximum. Each
k-magnitude pixel, which is adjacent to an already labelled watershed region, is added into that
region. A new region is constructed for the pixel which is not connected to any existing regions.
As mentioned above, the watershed transform always produces an over-segmented output due
to the presence of spurious local minima. In order to remove these local minima, the edge
magnitude image is thresholded prior to the watershed transform:
 g x , y  if g x , y   T
I x , y   
.
(1)
otherwise
T
The variable g(x, y) denotes the edge magnitude value of a pixel located at coordinate (x, y) and T
is the user-defined threshold. The thresholding replaces all lower magnitude values by the uniform
threshold T and small regions can then be eliminated by removing insignificant local minima.
2.4 Region Merging
To further remedy the over-segmentation problem, a modified hierarchical stepwise optimisation
(HSWO) algorithm is proposed here. This modified version follows closely the original
framework (Beaulieu and Touzi, 2004), except for the use of the Roy’s largest eigenvalue as a
stepwise criterion. The modified HSWO algorithm involves four processing steps. It proceeds
iteratively as follows:
Step 1: The segmented output resulting from the watershed transform is used as an initial image
partition.
Step 2: For each adjacent pair of regions i and j, compute the stepwise criterion:
 1
1 
1
SC  


.
(2)
 Ni N j 

1

1

 max ch1 Ci C j ,ch1 C j Ci
 
 

Step 3: Perform the globally best merging. To achieve this, find and merge the spatially adjacent
region pair with the maximum stepwise criterion value over the entire image.
Step 4: The process is terminated if no merging is needed; otherwise, go to Step 2. The
termination depends on the preset desired number of regions.
As can be seen in Equation (2), the stepwise criterion consists of two terms. The first term favours
the merging of small regions, where Ni and Nj refer separately to the size of the two regions i and j.
The larger the region sizes Ni and Nj, the smaller the value of 1 N i  1 N j . The second term
measures the dissimilarity of the two regions. Both Ci and Cj are the average covariance matrix of
the two regions i and j, respectively. The eigenvalue ch1 C i C j 1 refers to the largest eigenvalue
of Ci C j 1 and max denotes the maximum operator. If both Ci and Cj are identical, the second term
is equal to unity due to the fact that both ch1 C i C j 1 and ch1 C j C i1 are unit eigenvalues. In
contrast, the second term is less than unity for Ci  Cj.






3. Experiment and Discussion
In this section the details of both the NASA/JPL POLSAR data and simulated POLSAR data are
first given, followed by the discussion on segmentation and classification results.
3.1 Experimental Data
The nine-look dual-frequency NASA/JPL POLSAR data (CCT ID: CM6419) were acquired on
19th September 2000 using the AIRSAR imaging system onboard a DC-8 research aircraft. The
aircraft flew at approximately 8 km altitude during the PACRIM-2 science mission. The look
angles were 26 and 62.5 corresponding to the near and far ranges of 9 km and 17 km,
respectively. Only the C- and L-band data were examined in this study since the P-band suffers
from undesirable radio frequency interference. The test data cover a coastal plain in the north-west
of Peninsular Malaysia, which is situated at Kuala Muda of Kedah state. It is bounded between
5 36’ N and 5 44’ N latitude as well as between 100 23’ E and 100 29’ E longitude. Being an
active agricultural zone, large portions of the area are irrigated land, which is cultivated mainly
with rice paddy crops. Both rubber and oil palm trees are planted on a moderate scale. Mangrove
forests are found along both riversides of Sungai Merbok.
In order to evaluate effectively the performance of the extended hybrid segmentation algorithm,
four multi-look POLSAR datasets were simulated following the procedure suggested by Lee et al.
(1994, see Appendix). These datasets included four-look C-band, four-look L-band, nine-look
C-band, and nine-look L-band. Each simulated set consists of 400 columns and 150 rows. Figure 2
presents the simulated POLSAR data containing four different land cover classes, namely rubber,
oil palm, scrub-grassland, and rice paddy.
3.2 Segmentation
The capabilities of the extended hybrid segmentation algorithm were first examined using
multi-look simulated POLSAR datasets and then with the NASA/JPL POLSAR data. As shown in
Figure 3, the extended hybrid segmentation algorithm produced satisfactory results, where all
simulated datasets were successfully segmented into the desired eight regions. It was observed
that the region boundaries were slightly better delineated in the L-band compared with the C-band.
In addition, the increasing number of looks helped to improve the segmentation results. For the
NASA/JPL POLSAR test data, five different region numbers were heuristically chosen and
examined, i.e. 10000, 25000, 50000, 100000 and 250000. Figures 4 and 5 show the subsets of the
selected outputs, respectively. It was found that the segmentation outputs relied strongly on the
user-specified number of regions. As indicated by the yellow coloured arrows in Figures 4 and 5,
the segmentation errors occurred in both the C- and L-band outputs when the desired number of
regions was set to 10000. From the segmentation outputs, it was noticed that some pixels in the oil
palm plantation area were wrongly merged into the same region from the neighbouring
agricultural land. The oil palm plantation area is labelled with “A”, whereas the agricultural land
is given the label “B” in the figures.
3.3 Classification
In n-look POLSAR data, each pixel consists of a 33 Hermitian polarimetric covariance matrix:
2




S HH
2 S HH S HV
S HH S VV




2


C   2 S HV S HH
2 S HV
2 S HV S VV
(3)
,


2


2 S VV S HV
S VV
 S VV S HH



where Srt denotes the scattering element of the received polarisation r and transmitted polarisation
t. The subscripts H and V represent horizontal and vertical polarisations, respectively. Goodman
(1963) showed that a matrix A  nC obeys a complex Wishart distribution. Based on this result,
Lee et al. (1994) derived a distance measure and introduced the so-called supervised complex
Wishart classifier for multi-look POLSAR data. The distance measure is defined by


(4)
d C,C m   ln C m  tr C m1C .
The matrix C is the covariance matrix for a candidate pixel p, while Cm is the average covariance
matrix of a target class m. The operators |  | and tr denote the determinant and trace of a matrix,
respectively. The pixel p is assigned to the nearest class.
By assuming that the POLSAR data of different frequencies are statistically independent, the
distance measure in Equation (4) can be generalised for classifying multi-frequency POLSAR
data (Lee et al., 1994). The generalised distance measure is given as follows:


d C n , C m,n    ln C m,n  tr C m1,n C n ,
N
n 1
(5)
where Cn is the covariance matrix for a candidate pixel p in the n-th frequency band. The matrix
Cm,n refers to the average covariance matrix of a target class m in the n-th frequency band. The
variable N denotes the total number of frequency bands.
In this study the complex Wishart classifier was employed to classify dual-frequency fully
polarimetric SAR data and single-frequency fully polarimetric SAR data. The supervised
classification was performed on per-pixel and per-segment bases. The inputs for the per-pixel
classification were both the unfiltered and filtered C- and L-band data. The filtered C- and L-band
data resulting from the spatially adaptive filter (Lee and Bretschneider, 2007) with a 77 window
were selected for the experiments. In the per-segment classification, the segmented outputs from
the hybrid segmentation algorithm with 100000 target regions were tested for each frequency
band. The covariance matrix of each pixel in the segmented output was the average covariance
matrix of the region which the pixel belongs to. Figures 6 and 7 show the per-pixel and
per-segment classification outputs. In the following, the discussion on the classification results is
divided into three parts: 1) assessment of classification accuracies, 2) effect of radar frequencies
as well as 3) comparison between per-pixel and per-segment approaches.
3.3.1 Assessment of Classification Accuracies
To evaluate the classification outputs, the existing topographical and land use maps as well as
optical remotely sensed images (i.e. Landsat-5 TM, MASTER and ASTER data) were used for
locating test samples. A field work was carried out in order to validate the suitability of the test
samples. Table 1 tabulates the computed overall accuracies and Kappa statistics. As expected, the
classification results using the unfiltered data were relatively poor. The overall accuracies of both
the C- and L-band were 35.09% and 42.16%, respectively. The improved results were obtained by
using the filtered data, where the overall accuracies were separately 62.78% and 66.95% for both
the C- and L-band. A further improved accuracy of 74.82% was attained by using a dual-band
input, i.e. the combination of both the C- and L-band. The increase in accuracy was due to the
complementary information of both the C- and L-band for classifying mangrove forest and rice
paddy classes. In the C-band alone, the mangrove forests were found to be largely misclassified
into the rubber category, while the rice paddy fields were mistakenly classified as water bodies in
the L-band.
In the experiments, some difficulties were encountered in classifying the diversified cropland,
dryland forest, fish farm, mudflat and road segments. The classification difficulties were mainly
caused by the poor separation between these and other land cover classes. In the C-band, the
diversified cropland was erroneously classified into the rubber category, while it was assigned
into the mangrove forest and oil palm categories in the L- band. For the dryland forest, it was
wrongly grouped into the mangrove forest and rubber categories in the C-band, whereas only into
the rubber category in the L-band. The fish farms were improperly classified as bareland and
marshland in the C- and L-band, respectively. For the road segments, the misclassification into the
bareland category was observed. The mudflat was misclassified as the rice paddy fields and water
bodies separately in both the C- and L-band.
3.3.2 Effect of Radar Frequencies
From the results, it was found that the effectiveness of POLSAR data for land cover classification
relies on the radar frequencies. For example, the rice paddy fields in the C-band were better
classified than in the L-band. The rice crops became transparent in the L-band imaging due to the
shorter crop heights compared with the L-band wavelength (i.e. 24 cm). The scattering
mechanisms over the rice paddy fields in the L-band were almost entirely contributed by the water
surface. Hence, the rice paddy fields were partially misclassified as water bodies. For the
mangrove forests, a better classification result was obtained using the L-band data. With the
longer wavelength, the L-band showed a stronger radar penetration in both mangrove forests and
rubber plantation areas. Subsequently, the L-band provided a more distinct discrimination
between both mangrove forests and rubber plantation areas.
3.3.3 Comparison between Per-pixel and Per-segment Classification Approaches
It is well-known that per-pixel classification approaches always produce classification outputs
with salt-and-pepper effects over homogeneous areas. To mitigate theses effects, per-segment
approaches or post-classification operations are normally employed. In this study, the
salt-and-pepper effects were observed in the C- and L-band per-pixel classification results. The
per-segment classification approach was found to improve the C-band classification accuracy.
The overall accuracy of the C-band per-pixel output was 62.78%, while it increased to 68.01% for
the per-segment classification. For the L-band classification outputs, only a slightly increase in
accuracy was observed. An overall accuracy of 66.95% was obtained from the per-pixel L-band
result, while the computed overall accuracy was 68.47% for the per-segment. As expected, the use
of the dual-band inputs in the per-segment classification produced the better accuracy, which
resulted in an overall accuracy of about 76%.
4. Conclusions
In this study an existing hybrid segmentation algorithm, which uses both edge and region
information, was extended for multi-look POLSAR data. Applied to NASA/JPL POLSAR C- and
L-band data, the segmentation outputs were found to rely on the user-defined region number,
which is employed as the termination rule. In land cover classification, an improved accuracy was
attained by using the dual-frequency input. As expected, the comparison between per-pixel and
per-segment approaches showed that the latter improved the classification accuracy, where the
salt-and-pepper effects were reduced significantly in the classification outputs.
References
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Title of table
Table 1: Assessment of per-pixel and per-segment classification results
Table 1: Assessment of per-pixel and per-segment classification results
Percent of correct classified into
Bareland
Bridge
Built-up
Mangrove forest
Marshland
Oil palm
Rice paddy
Rubber
Scrub-grassland
Water body
Overall accuracy
Kappa statistic
Unfiltered
C-band
27.42
53.25
80.97
12.76
36.45
30.20
74.20
27.80
65.52
99.77
35.09
0.2742
Per-pixel classification
Unfiltered
Filtered
Filtered
L-band
C-band
L-band
32.21
33.11
40.02
72.19
67.46
84.02
66.85
85.33
79.90
45.65
44.05
84.46
36.17
78.03
74.15
45.77
82.07
93.72
19.16
85.46
29.36
69.48
81.53
99.69
47.12
99.59
57.97
99.52
100.00
99.81
42.16
62.78
66.95
0.3403
0.5685
0.6144
Filtered Cand L-band
56.74
91.12
87.90
89.10
92.38
95.65
59.77
99.88
83.65
99.94
74.82
0.7046
Per-segment classification
C-band L-band C- and L-band
52.39
71.01
88.37
50.06
77.89
94.36
86.35
89.95
100.00
100.00
68.01
0.6281
35.78
82.25
83.96
92.77
87.94
99.62
18.21
99.93
62.36
100.00
68.47
0.6323
83.39
86.39
96.36
91.52
94.39
100.00
58.36
100.00
85.02
100.00
76.60
0.7251
Titles of Figures
Figure 1. 33 edge templates with different orientations.
Figure 2. Simulated POLSAR data.
Figure 3. Results from the extended hybrid segmentation algorithm.
Figure 4. C-band outputs resulting from the extended hybrid segmentation algorithm.
Figure 5. L-band outputs resulting from the extended hybrid segmentation algorithm.
Figure 6. Complex Wishart classification results.
Figure 7. Complex Wishart classification results obtained from NASA/JPL POLSAR
dual-frequency data.
Figure 1. 33 edge templates with different orientations (a) 0º, (b) 45º, (c) 90º and (d) 135º.
(a)
(b)
(c)
(d)
Figure 2. Simulated POLSAR data. (a) and (b) are the four- and nine-look C-band, while the fourand nine-look L-band are given in (c) and (d). For each image, the HH, HV and VV intensities are
displayed in the RGB colour space. The areas of scrub, rice paddy, oil palm and rubber are
labelled as A, B, C and D, respectively.
Four-look C-band
Nine-look C-band
Four-look L-band
Nine-look L-band
Figure 3. Results from the extended hybrid segmentation algorithm.
Watershed transform input
100000 regions
50000 regions
10000 regions
Figure 4. C-band outputs resulting from the extended hybrid segmentation algorithm. Note that
each display is only part of the entire processed output.
Watershed transform input
100000 regions
50000 regions
10000 regions
Figure 5. L-band outputs resulting from the extended hybrid segmentation algorithm. Note that
each display is only part of the entire processed output.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 6. Complex Wishart classification results. Per-pixel classification outputs of (a) C-band
unfiltered data, (b) C-band filtered data, (d) L-band unfiltered data, and (e) L-band filtered data.
Per-segment classification outputs of (c) C- and (f) L-band segmented data. Please refer to Figure
7 for the legend.
Legend
(a)
(b)
Figure 7. Complex Wishart classification results obtained from NASA/JPL POLSAR
dual-frequency data. (a) and (b) are, respectively, per-pixel and per-segment classification
outputs.