383 
d ,d = area of specified "unit area" of dimension d in azimuth 
Qa T oa 
and dor in range. 
el = Capacity relvant to "unit area". 
La» Lp = number of looks in respectively azimuth and range 
direction relevant to d4 and dr: 
Loa». Lor * number of looks in respectively azimuth and range 
direction relevant to dg4 and dor: 
Yas Yp 7 look overlap in respectively azimuth and range 
direction. 
The results of (19) are depicted -in figure 1b for the condition L,. = 1, 
Loa = 4,,Ya = Yr = 1/3, La = 4Lr and the total number of looks L = La 
Lr = 4Lpefor p = 0.7 and r 
variance image). 
3 (high variance image) and r = 9 (low 
Figures la and 1b show that the information content per unit area is low 
for relatively low number of looks. Taking the typical case of the Seasat 
or ERS-1 radar sensor, when the range and azimuth resolution of the 
processed data is approximately 25m, it is evident that for a typical 
correlation coefficient of 0.7, the information content is only 0.2 - 0.35 
bits per resolution square area with four look processing. Note that this 
is only about 50% of the information per unit area provided by an optica! 
sensor using 8 bit pixel quantization with a spatial resolution of 80x80m 
(like Landsat III). In other words the ratio between the information 
content per 8 bit sample of an optical and a radar sensor is at least 15 
to 1. 
4. RADIOMETRIC RESOLUTION 
  
Rate distortion theory learns that for an information source with variance 
c, transmitting through a communication channel with C bits/sample 
capacity, the minimum mean square error of the reconstructed signal is 
given by: 
D(C) m (20) 
Applying (17) to each of the channels with the transform coefficient 
Z(n,2) as information source, the channel capacity is, using (15) and 
{125}: 
C(n,2) > 3 1092| —z— (21) 
2 qM 
where: o (n,2) » c Sn B. 
The minimum error per channel is then upperbounded by: 
c?(n , 4) o* 
p (22) 
a, 8) sac. 
D(n,2) = 7 
ag