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Land Use/Land Cover
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Land cover classification and interpretation of NASA / JPL AIRSAR data based on scattering mechanisms and statistical distribution
Results and Discussion
In Figure 2(b) showing the results of unsupervised classification based on the scattering mechanisms,
the rice paddy and built-up area in L-band data were categorized into the even-bounce class whilst the
rubber plantation was dominated by diffuse scattering. The grassland, highway, and runway which
exhibit specular scattering fell into the odd-bounce class. The canal and river gave both odd-bounce and
diffuse scattering. While single bounce was expected due to scattering from the water surface, the
diffuse scattering was mainly contributed by the trees and scrubs along the sides of the waterways. In
this study, it was found that both rice paddy and built-up area in L-band data could be further separated.
The paddy showed higher backscattered intensity in VV (versus HH) whilst HH has higher intensity in the
built-up area. The result obtained was identical to Yamada and Hoshi (2001). For the C-band shown in
Figure 2(a), all land cover features, excluding built-up area, were characterized as having the odd-bounce
scattering behavior. The runway was successfully separated from the surrounding grassland
through the comparison of |HH| 2and |VV| 2
Table 1 presents the percent of scattering mechanisms of
land cover classes in both the C- and L -band data.
As can be seen in Figure 3(b), the paddy and built-up area in L-band data were classified as having
multiple scattering. The major scattering mechanism of the paddy belonged to the type of low entropy
scattering whilst the built-up area, as expected, showed both low (25%) and medium (48%) entropy
scattering. In L-band, the rubber was dominated by high entropy vegetation scattering while it exhibited
the medium entropy surface scattering in C-band. The runway was categorized into the class of medium
entropy surface scattering for both C- and L-bands. For the canal and river, the dominant scattering was
medium entropy vegetation scattering. The grassland gave medium entropy vegetation (50%) and
surface (61%) scatterings respectively in both C- and L-bands. The percent of scattering mechanisms of
land cover classes, derived from the target decomposition for C- and L -band data is given in Table 2.
For the supervised classified results, the overall accuracy and Kappa statistics (Congalton and Green,
1999) were computed and are tabulated in Table 3. The classification results obtained were promising
where the correct classification of land cover classes was more than 70% for both C- and L-bands. With
the longer wavelength, the L-band data showed more distinct discrimination between land cover classes
and thus gave the better classification performance compared to the C-band. Nevertheless, from
Figures 4b and 4e, it was clearly observed that the highway was misclassified into the waterbody class
due to their poor separation in both C- and L-band data. An improved accuracy of 83% was derived from
the combined dual frequency (C and L bands) fully polarimetric SAR data. For the single-frequency dual
polarization complex images, the HH and HV combination in the C-band and the L-band HV and VV
combination gave better results compared to the other polarization compositions. Only a limited number
of classes could be discriminated from the C- or L-band single intensity images. The HV polarization
showed its excellence in classifying both the rice paddy and rubber plantation classes, but was not good
for discriminating built-up area compared to HH and VV polarizations. Among all polarizations, HH
polarization produced the highest accuracy.
Conclusions
In this study, we have shown the unsupervised classification of NASA/ JPL POLSAR data through two
different approaches: van Zyl method and Cloude and Pottier’s target decomposition. Also, the various
frequency and polarization combinations of POLSAR data were investigated in the supervised
classification. For single-frequency and single-polarization SAR intensity data, the supervised Maximum
Likelihood classification was performed based on Gamma distribution. Whilst, the supervised complex
Wishart classifier was used to classify the following inputs: multifrequency fully polarimetric SAR, single-frequency
fully polarimetric SAR, and dual-polarization complex SAR. The C- and L-band POLSAR data
have proven to be useful in land cover classification, especially for paddy class. An overall accuracy of
78% was shown by the L-band while the accuracy achieved by C-band was 75%. The use of the
combined C- and L-bands fully polarimetric data, as expected, increased the accuracy to 83%. Only a
limited number of land cover classes could be discriminated from C - and L -band intensity data.
Acknowledgement
The NASA / JPL is gratefully acknowledged for providing the POLSAR data.
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