International audienceThis work addresses the potential of multi-band polarimetric SAR imaging for terrains and vegetation classification. A classic supervised Wishart classifier is adapted to polarimetric multi-band datasets, and is applied on the X-, Land UHF-band acquisitions done during the NAOMI campaign (ONERA-Total) in Gabon (Africa) in 2015. The contributions of the different frequencies are shown and discussed. It is shown that the use of the multi-band dataset improves significantly the classification result

Alakian, Alexandre

Dubucq, Dominique

Dupuis, Xavier

Wasik, Valentine

English

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Multi-band supervised classification for polarimetric
SAR
Xavier Dupuis, Valentine Wasik, Alexandre Alakian, Dominique Dubucq
To cite this version:
Xavier Dupuis, Valentine Wasik, Alexandre Alakian, Dominique Dubucq. Multi-band supervised
classification for polarimetric SAR. IGARSS 2019, Jul 2019, YOKOHAMA, Japan. ￿hal-02339219￿
MULTI-BAND SUPERVISED CLASSIFICATION FOR POLARIMETRIC SAR  
 
Xavier Dupuis (1), Valentine Wasik (1), Alexandre Alakian (2), Dominique Dubucq (3) 
 
(1) : DEMR, ONERA, F-13661 Salon cedex Air - France 
(2) DTIS, ONERA, Université de Paris Saclay, F-91123 Palaiseau - France 
(3) TOTAL S. A., Centre Scientifique et Technique Jean Féger, 64000 Pau - France. 
 
ABSTRACT 
 
This work addresses the potential of multi-band polarimetric 
SAR imaging for terrains and vegetation classification. A 
classic supervised Wishart classifier is adapted to 
polarimetric multi-band datasets, and is applied on the X-, 
L- and UHF-band acquisitions done during the NAOMI 
campaign (ONERA-Total) in Gabon (Africa) in 2015. The 
contributions of the different frequencies are shown and 
discussed. It is shown that the use of the multi-band dataset 
improves significantly the classification result. 
 
Index Terms— SAR, classification, multi-band 
 
1. INTRODUCTION 
 
The NAOMI project (New Advanced Observation Method 
Integration) is a Total and ONERA partnership initiated in 
2014. Among the objectives of this project, one is to analyze 
the capacities of remote sensing techniques to characterize 
surfaces (biophysical and geological) and to detect 
hydrocarbons (directly or indirectly through environmental 
descriptors such as vegetation health and state). In this 
context, the NAOMI campaign that took place in Gabon in 
2015 allowed ONERA and Total to construct a particularly 
diversified dataset composed by polarimetric SAR and 
VNIR-SWIR hyperspectral acquisitions along with in-situ 
measurements over the Mandji peninsula. This site presents 
an alternation between dense tropical forests, sparse and 
short vegetation, backwaters and man-made structures, with 
oil deposits that have been exploited for over 50 years in 
this area. On such equatorial area, optical acquisitions are 
difficult because of clouds and heavy rains season. Radar is 
a good alternative to overcome the weather problem. 
Moreover the swath of a SAR sensor is wider than optical 
one, which makes SAR imaging more suitable to cover large 
areas in such a place. Landscape classification from SAR 
imaging seems to be an interesting technique to characterize 
terrains and vegetation for the purpose of the NAOMI 
project. Moreover, the Mandji dataset gives us the 
opportunity to explore the contribution of several bands in 
the results of the classification products, since acquisitions 
have been done in X-, L- and UHF-band. In this work, we 
propose to adapt and apply a classic supervised Wishart 
classifier [1] to our multi-band SAR dataset. After a brief 
presentation of the data, the classifier methodology is 
explained. The reference regions from in-situ measurements 
are then detailed, and the classification results are finally 
discussed. 
 
2. DATASET 
 
Data were acquired with SETHI [1], an ONERA sensor, in 
Gabon over Mandji area in 2015, July 8
th
. Two flights were 
performed, one to acquire simultaneously X and L bands 
and a second to acquire simultaneously UHF and L bands. X 
and UHF bands were acquired in full polarimetric mode. L-
band was acquired in dual-polarimetric mode. Table 1 
summarizes the characteristics of the data. 
 
SAR data 
UHF/VHF 
band 
L band X band 
Bandwidth 200 MHz 150 MHz 300 MHz 
Range 
resolution 
0.66m 0.88m 0.44m 
Azimuth 
resolution 
1.33m 0.88m 0.44m 
Windowing rectangular rectangular rectangular 
Polarization full Dual Hh-Hv full 
Table 1: SAR dataset main characteristics. 
Figure 1 to Figure 3 present the SAR images of the area at 
X-band, L-band and UHF-band respectively. 
 
 
Figure 1: X-band data, Hh polarization is coded on the Red 
channel, Vh on the Green channel and Vv on the Blue one. 
 
 
Figure 2: L-band data, Hh polarization is coded on the Red 
channel, Hv on the Green channel and Hh on the Blue one. 
 
 
Figure 3: UHF-band data, Hh polarization is coded on the 
Red channel, Vh on the Green channel and Vv on the Blue 
one. 
The SAR dataset is coregistered and projected into the X-
band native slc geometry for classification purpose. 
Flights dedicated to optical acquisitions were operated the 
same day. The characteristics of the hyperspectral cameras 
are presented in Table 2. 
 
 
Hyspex VNIR 
FOV expander 
Hyspex SWIR 
Imaging Pushbroom Pushbroom 
Spatial pixels 1600 320 
Field of view 34° 14° 
Spectral range 0.4 – 1 µm 1 – 2.5 µm 
Spectral bands 160 256 
Spectral sampling 
distance 
3.7 nm 6 nm 
Ground sampling 
distance 
1.3m 2.6m 
Table 2 : Hyperspectral cameras characteristics. 
 
 
Figure 4 : Optical image of the Mandji site. 
 
SAR and hyperspectral (VNIR, SWIR) data are ground-
coregistered [3] in a common WGS84 referential. 
 
3. MULTIBANDS CLASSIFIER 
 
The Wishart classifier is an interesting and largely used 
technique for the classification of the landscapes with 
polarimetric SAR images [2]. This iterative method focuses 
on the statistical properties of the response. For fully-
polarimetric SAR data, the complex response for each pixel 
can be written in the Pauli vector 𝒌 = 1
√2
[𝑆𝐻ℎ + 𝑆𝑉𝑣 , 𝑆𝐻ℎ −
𝑆𝑉𝑣 , 2𝑆𝐻𝑣]
𝑇, where 𝑆𝑋𝑦 is the complex backscattered signal 
for an emitted polarization 𝑋 and a direction of reception 𝑦 
(𝑋 = 𝐻, 𝑉 ; 𝑦 = ℎ, 𝑣). In case of fully-developed speckle, 𝒌 
is assumed to be a random vector following a complex 
circular multi-variate Gaussian law with zero-mean for 
which the statistical properties are entirely described by its 
covariance matrix 𝐓 = 〈𝒌𝒌†〉. For a set of 𝑁 measurements 
{𝒌1, … , 𝒌𝑁}, the maximum-likelihood estimator for 𝐓 (ie 
?̂? = 1
𝑁
 ∑ 𝑘𝑛𝑘𝑛
†𝑁
𝑛=1 ) follows a complex Wishart law [1]. The 
principle of the Wishart classifier is to use this property to 
construct a measure for classification. For each pixel of the 
image and its associated empirical covariance matrix ?̂?, the 
following measure is calculated for each class 𝑤𝑖  [1]: 
𝑑𝑊(?̂?, 𝑤𝑖) = ln|𝐕𝑖| + 𝑡𝑟(𝐕𝑖
−1?̂?), where 𝑉𝑖 is the covariance 
matrix of the pixels in the class (i.e. class center), |. | is the 
determinant, and 𝑡𝑟(. ) is the trace operator. The pixel is 
assigned to the class 𝑤𝑗  for which the Wishart measure is 
the smallest. This algorithm is iterative. In our study the 
initialization is based on the supervised definition of the 
classes and for each step the class centers are updated. The 
algorithm is stopped after 5 iterations. 
In the Mandji data presented, the acquisitions have been 
done with three different frequency bands: X, L and UHF. 
The enhanced behaviors of the scene will be different from 
one frequency band to the other since the interaction 
between the media and the illuminating wave is very 
frequency dependent. The contribution of this frequency 
diversity in the classification process is an interesting point 
to focus on. For this purpose, we have modified the Wishart 
classifier so that multi-band polarimetric SAR data could be 
processed. For each pixel, the idea is to estimate one 
covariance matrix for each considered band (?̂?𝑋 , ?̂?𝐿 , ?̂?𝑈𝐻𝐹) 
and to calculate independently the associated Wishart 
measures for a common ensemble of classes. The pixel is 
attributed to the class 𝑤𝑗  that minimizes the sum of the three 
measures: 
𝑤𝑗 = min
𝑖
(𝑑𝑤
𝑋 (?̂?𝑋 , 𝑤𝑖) + 𝑑𝑤
𝐿 (?̂?𝐿 , 𝑤𝑖) + 𝑑𝑤
𝑈𝐻𝐹(?̂?𝑈𝐻𝐹 , 𝑤𝑖)). 
The algorithm is then iterated as previously. 
One difficulty comes from the fact that the L-band 
acquisitions are dual-polarized (emission in 𝐻 only). To 
overcome this limitation, the coefficient 𝑆𝑉𝑣
𝐿  in the Pauli 
vector for the L-band is replaced by the ratio 𝑆𝐻𝑣
𝐿 𝑆𝐻ℎ
𝐿⁄ . 
 
4. GROUND TRUTH 
 
Ground truth is based on in-situ photography and ASD 
reflectance ground measurements. These measurements are 
used to identify the classes, but they do not cover a 
sufficient area to get a reference ground truth map for the 
classification (see Figure 5 (a)). To extend the ground truth 
map, a classification on both VNIR and SWIR data taking 
into account the shape similarities of spectral reflectance 
(spectral angle), the reflectance amplitude similarities and 
the specific absorption is performed. The spectral angle 
criterion is the most sensitive parameter, a strict constraint is 
1° threshold and less strict is 3°. The results are presented in 
Figure 5. 
 
(a) 
 laterite 
 Water 
 Short stressed 
vegetation 
 Sparse green 
vegetation 
 Dense green 
vegetation 
 Sand 
 Old backwater 
 Tree 
(b) 
(c) (d) 
Figure 5 : Ground truth map : (a) in-situ ground truth (from 
ASD measurement), (b) classes definition, (c) computed 
map with 1° spectral angle threshold and (d) computed map 
with 3° spectral angle threshold. 
 
5. SAR CLASSIFICATION RESULTS 
 
Prior to the classification, a bilateral filtering is processed on 
each SAR images to attenuate the speckle noise [4]. The 
classification algorithm studied in this paper is supervised, 
so we first define the reference classes based on the 
extended ground truth map (Figure 5 (c)). The “old 
backwater” class was not considered for SAR classification 
since it was not sufficiently different from other classes. A 
class of “shadow” (colored in black in the following) has 
been added due to the particularity of the SAR acquisition 
geometry as well as a class for “man-made structures” in red 
A classification is performed for each band independently 
(X, L, and UHF) to analyze the impact of each radar band 
on the results. Then multi-band classifications are processed 
for “X plus L” and “X plus L plus UHF” combinations. 
Note that all the results presented below are based on the 
same class definition. 
Figure 6 presents the classification result for X-band dataset. 
It appears noisy because X-band is very sensitive to 
roughness of small objects, accordingly to its wavelength, 
and cannot distinguish vegetation types as trees, dense and 
short vegetation. At the opposite, the water (sea and 
backwater in blue), and the sand (in grey) are well detected. 
 
 
Figure 6 : X-band classification. 
Figure 7 presents the L-band classification result. Compared 
to X-band, there are confusions for water (sea and 
backwater) but vegetation is less noisy except for the trees 
(dark green). 
Figure 8 presents the UHF classification result. Except for 
the strong echoes from buildings and trees (red class) and 
for the sand on the beach, the classification is very noisy due 
to non-sensitivity of such a wavelength to short and medium 
vegetation. 
 
 Figure 7 : L-band classification. 
 
 
Figure 8 : UHF-band classification. 
A first result of multi-band combination is presented Figure 
9. Qualitatively, the “X- plus L-“band classification seems 
to provide a good result as it takes advantages of both bands. 
The sea and backwaters appears qualitatively well 
classified; the different classes of vegetation seem close to 
the extended ground truth map of Figure 5 (d). But the 
buildings (in red) and the trees (in the white ellipse) are not 
very well delimited. 
 
 
Figure 9 : X+L band classification. 
As it can be seen on Figure 10, combining the three bands, 
X, L and UHF, improves significantly the classification of 
tall vegetation (in the white ellipse) and building (in red). 
This result takes advantage of the different bands: X-band 
for water and bare surfaces, L-band for short and medium 
vegetation and UHF-band for tall vegetation and buildings. 
 
 
 
Figure 10 : X+L+UHF band classification. 
 
6. CONCLUSION 
 
For the Mandji dataset acquired by ONERA during the 
NAOMI campaign, multi-band SAR classification improves 
significantly the results for vegetated areas with different 
height and state of vegetation in comparison with using only 
mono-band data. It takes advantages form the different 
wavelength. 
To complete this first qualitative analysis, a quantitative 
study with confusion matrices is going to be processed. 
 
7. REFERENCES 
 
[1] R. Baqué and al., SETHI / RAMSES-NG New 
performances of the flexible multi-spectral airborne remote 
sensing research platform, RADAR 2017, Belfast, 2017 
October 23-26. 
[2] J. S. Lee, M. R. Grunes, and K. Ri. (1994). Classification 
of multi-look polarimetric SAR imagery based on complex 
Wishart distribution, International Journal of Remote 
Sensing, 15(11), p.2299-2311. 
[3] A. Plyer, et al. (2015). A New Coregistration Algorithm 
for Recent Applications on Urban SAR Images, Geoscience 
and Remote Sensing Letters, IEEE, 12(11), p.2198-2202. 
[4] C. Tomasi, and R. Manduchi. (1998). Bilateral filtering 
for gray and color images, in Proc. 6th ICCV, Bombay, 
India, p.839. 


Multi-band supervised classification for polarimetric SAR

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Multi-band supervised classification for polarimetric SAR

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