Table 2. Mean, standard deviation and speckle index 
processed by 8 filters. 
  
  
  
  
  
  
  
Filter M.V. S.D. Speckle 
Index 
Original 187.54 57.94 0.309 
Box 186.42 18.94 0.102 
Median 186.84 19.08 0.102 
Lee 186.42 18.94 0.102 
Frost 186.44 19.17 0.103 
Kuan 186.42 18.94 0.102 
Enh. Lee 194.29 19.86 0.102 
Enh. Frost 194.30 19.36 0.100 
G-MAP 192.21 | 20.89 0.109 
  
In order to test the performance of filtering techniques 
for a different types of area, Table 3 is made below. 
Several test areas were selected, such as ocean, coastal 
line, etc. and testing results present the ability of 
filtering homogeneous areas and heterogeneous areas. 
The speckle index is chosen as a criteria to measure the 
reduction of speckle noise by these filters. The 
homogeneous and heterogeneous areas were measured 
by the speckle index. From Table 3, for the 
heterogeneous areas, the speckle index is relatively 
higher. The GMAP filter is much more efficient for 
reducing the noise while keeping the edges. 
Table 3 Speckle index test for different terrain. 
  
  
  
Filter From homo- to Hetero- 
geneous geneous 
Original 0.309 0.312 0.328 0.464 
Box 0.105 0.121 0.142 0.228 
Median 0.112 0.123 0.152 0.220 
Lee 0.105 0.121 0.142 0.228 
Frost 0.107 0.122 0.145 0.235 
Kuan 0.104 0.121 0.142 0.228 
Enh. Lee 0:112 0:125 0.152 0.328 
Enh.Frost | 0.107 0.125 0.147 0.301 
GMAP 0.126 0.128 0.167 0.384 
  
  
Qualitative Evaluation: 
The qualitative evaluation was made by visual 
interpretation. A 3-look ERS-1 image with the size of 
256 by 256 pixels was selected to test the performance 
of the filters. The test results is shown in Fig.4. Fig4(a) 
is the original image, which is an agricultural land area. 
Fig4.(b) illustrates the Box Filter filtered image. The 
speckle is reduced dramatically. However, the image 
also became blurred. The Median filter is slightly 
superior to the Box Filter for keeping edges(ref. 
Fig.4(c)). The Lee filter improves the ability of 
preserving edges compared with the box filter while 
reducing speckle noise(Fig.4.(d)). The Kuan Filter and 
Frost Filter are similar to the Lee Filter. Enhanced Lee 
Filter and Enhanced Frost Filter make improvements on 
reducing speckle noise at the edges. However, it cannot 
remedy sharp spot noise. GMAP Filter ptoved to be 
168 
  
much better for filtering speckle noise while preserving 
the edges. From a visual interpretation point of view, it 
is the best one. 
Fig.4, (a) Original ERS-1 3-look image.(b). Box Filter. (c). 
Median Filter. (d). Lee Filter. (e). Frost Filter. (f). Kuan 
Filter. (g). Enhanced Lee Filter. (h). Enhanced Frost 
Filter.(i). GMAP Filter. ‘ 
In order to evaluate the performance of the filters with 
different window size and different damping factors, 
which are parameters available in the PCI software 
package, we take the Frost Filter as an example. The 
tested results are shown in Fig.5. From Fig.5, we 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B2. Vienna 1996 
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