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Technical Commission VII


where ó,- Dirac-delta function, if k-0, ój-1; kZ0, 0,0.
c7 (c|c€ C, s€ c).
Therefore, from (5) and (7) we know:
Ws) » argmin Y (-In p(X | w(s))+ BY (1-6, (5,)]
w(s) ses ceC
3.1. Study Area and Database
The measures reports in this study are conducted during the
Watershed Airborne Telemetry Experiment. The study area
locates in Grass Station of Lanzhou University in Zhang Ye
district, Gansu province. Its geographical coordinates are
39.25043?N, 100.005871?E, the altitude is 1385 meters. Land
use mainly consists of country, bare salinization land and
irrigative agricultural fields. The field experiment was
conducted from June to July in 2008, at which time the crops
were corn, clove, barley and other crops.
Satellites over the study area provided TM and ASAR data
on 7 July 2008 and 11 July 2008, respectively. ASAR
(Advanced Synthetic Aperture Radar) is a synthetic aperture
radar carried by the ENVISAT-1 satellite and operates in the C-
band (central wavelength 5.63 cm), with multi-polarization,
seven observation angles and five operating modes. In this
study, we chose to use the ASAR data, and the operating mode
was Alternation Polarization corresponding to two kinds of
polarization (VV and VH) and high space resolution (12.5x12.5
m per pixel). Figure 1(a) and 1(b) illustrate the false color
composite image composed by TM3, 4, 5 and ASAR image in
VV polarization.

T ss fais E: i d
(a) The false color composite image (b) ASAR image in VV
Figure 1. The images used in the paper
When the initial class labels and conditional probability
density function of the multi-source remote sensing data are
determined by MLMM, formulation (10) is used to perform the
local minimization at each pixel in a specified order and get the
updated category. If changes occur then repeat estimating. The
iteration continues until no more updates occur for all the pixels
inside the lattice, then, the classification completes.
Comparing with the conventional Markov model for iterative
classification, our method needn't to assume the conditional
probability density function in advance; With joining the spatial
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
correlation of the class labels, our method also can get a better
classification accuracy than the ordinary maximum likelihood
classification model. Moreover, the classification is achieved
through the iterative process, which takes into account the
characteristics of pixel attributes.
3.2. Classification experiment
VV, VH polarization of ASAR and all the TM bands are taken
as the input of the classifier, and the multisource images are
resampled to 30m*30m and geometrically corrected. The study
area is separated to 12 classes, which are corn, other corps,
garden, woodland, meadow, fallow land, sand, mountain, saline,
desert, building, and water. The training samples and validation
samples are shown in Table 1. When the training samples and
validation samples are selected, we use the method in Section 2
to classify the TM and ASAR images, the results are shown in
Figure 2, In which, the basic form of oasis is similar with the
Figure 1(a), which is consistent with the dual ecological
environment of western semiarid regions, “oasis accompanies
with water, and desert accompanies with no water”.
Fhe Classification of Heihe Bivel By ASAR and TM
Figure 2. The Classification map of study area
i} ;
LM tomes
3.3. Validation
To verify the necessity of coupling optical radar data for
classification, the output of the classification of ASAR and TM
in Section 3 were compared with the classification only by TM,
all using Bayesian and MRF classifier. Table 2 presents the
statistical errors among the three algorithms.
In Table 2, the accuracy of each type of classification with
single TM is lower, and reaches a total precision of 77.9%.
When the ASAR dual polarization is jointed, the total precision
increases to 89.496. The reason may be that the ASAR
information can increase the surface characteristics and make
them easy to distinguish, for example, corn, other corps, garden
and woodland are similar in spectrum, and we can identify them
by their various structural features revealed by their ASAR
backscattering coefficients and finally obtain a better accuracy.