Figure 5. Scattergram of radar (SIR-B) incident angle 
43° versus 36°. Mean response values of Forest (F), 
mature residential (R), soil {S), intense urban (I) 
(commercial and industrial), and cardinally aligned 
residential (C) are shown with an ellipse represent 
ing two standard deviations. 
Figure 6. Scattergram of Landsat Band 7 versus radar 
(SIR-B) incident angle 36°. Mean response values of 
Forest (F), mature residential (R), soil ,(S) , intense 
urban (I) (commercial and industrial), and cardinally 
aligned residential (C) are shown with an ellipse re 
presenting two standard deviations. 
the response from different incident angle radar 
could discriminate between urban classes due to the 
differing combinations of specular, diffuse and 
corner reflector back scattering. Possible variations 
in backscatter with incident angle, are illustrated 
in figure 4 for various idealised urban surfaces. 
The scattergram of incident angle 36° versus 43° is 
shown in figure 5, and Landsat 7 versus 36° in figure 
6, for cleared and heavily urbanised surfaces. 
Similar difficulties with Landsat data have occurred 
when attempting to discriminate older residential 
areas, that typically are surrounded and overhung by 
mature vegetation, from low density forest which has 
a predominantly vegetation signature, modified by soil 
and understorey, in the visible and the near infrared. 
Because the SIR-B wavelength of 23.5 cm (L-Band) has 
a reasonable penetrating capacity it was expected that 
an increased response from the buildings underlying 
the trees would result, allowing the separation of 
these confused cover classes. Examples of their res 
ponse from Landsat and SIR-B are also shown in the 
scattergrams, figures 3, 5 and 6. Note that units 
used in the scattergrams are count values on a 0 - 255 
scale as recorded. 
4. DISCUSSION OF RESULTS 
From the scattergrams (figures 3, 5 and 6) it can be 
seen that the radar response, for both incident angles, 
has a much greater range of values than the equivalent 
Landsat response, and the spread of values for indivi 
dual cover types is again much greater for radar. The 
greater overall range of radar values is due to the 
increased number of surfaces that act in a specular 
manner. Whilst the majority of surfaces in a Landsat 
urban scene respond in a near diffuse manner, for 
high sun angles, surfaces of roughness variation less 
than approximately 3 cm, for L-Band radar, will cause 
specular reflection. Such surfaces can include 
grass, concrete, bitumen and buildings. Thus, depend 
ing upon the relative angle between the incident 
radiation and the surface, either no response or a 
saturated response can result. This extreme dependence 
on the alignment of the surfaces also holds true for 
individual surface classes, where varying roof and 
building facets, and orientation of leaves and branches 
can cause considerable variation in radar backscatter 
from a surface that appears essentially homogeneous 
to Landsat. 
Examining the plot of Landsat Bands 5 and 7 as shown 
in figure 3, it can be seen that forest and mature 
residential surface classes overlap and their mean 
response values are only marginally separated by 
approximately 10 count values in Band 5, making it 
difficult to define a decision surface between them 
for classification purposes. However in figures 5 and 
6, while there is still some overlap, the mean values 
have a greater separation. The introduction of radar 
response has thus aided class discrimination. The 
situation with soils and the intense urban class of 
commercial and industrial land use, is not so clear. 
Whilst they are relatively well separated in the two 
dimensional space of Landsat Bands 5 and 7 (figure 3) 
this may not be the case when darker soils are invol 
ved because the Band 7 on Band 5 ratios of these sur 
faces response are very similar, and thus a darker 
soil response would move closer to, and possibly 
overlap, the intense urban response cluster. This is 
not the case in figure 6 where the lowered radar 
response of soils, due to its specular reflection 
away from the receiving antenna, and the high radar 
response of the intense urban surfaces allows for 
easier class separation with little possibility of 
overlap due to their significantly different ratios. 
A further surface 'cardinal residential' is also 
shown in figures 5 and 6. This represents tree 
covered residential areas where major street patterns 
are aligned at right angles to the incident radar 
radiation, resulting in the so called 'cardinal 
effect' discussed early. Because of the significantly 
higher backscatter from these areas, the separation 
between forest and residential is even more pronoun 
ced, even though the spread of values is considerably 
greater than for 'non-cardinal' residential. A 
similar effect would result from 'cardinally' aligned 
intense urban (industrial and commercial) surfaces, 
resulting in greater separation from soils, but 
examples of these were not available in the study 
area. 
It is clear from the scattergrans that the great 
est, difficulty in using radar backscatter in the 
classification of urban surfaces is the wide range 
of values displayed by each class. This internal 
class 'noise' could be reduced by the use of the 
mean value determined over say a three by th-atee 
neighbourhood. The resultant reduction in variance 
would significantly improve classification accuracy 
and have the added advantage of being/more spatially 
compatible with the Landsat data, £ut with the 
resultant disadvantage of loss of spatial detail for/ 
interpretation. 
5. CONCLUDING REMARKS 
While the results from this study are limited at