271.

[Cover] [Contents] [Index]

Page 42

(Mather, 1999a). Both of these filters have a speckle-suppression capability, but they also smooth away other high-frequency information. The median is more effective than the mean in eliminating spike noise while retaining sharp edges. Both filters are easily implemented and require less computation than adaptive filters. Figure 1.25 illustrates the application of both mean and median filters using a 3×3 window. The mean filter calculates the average value of all the pixels within a specified window, and assigns the mean value of 84 to the centre pixel. The median filter assigns the median value (66) of the pixels covered by the window.

In comparison with the mean filter, the median filter preserves step edge patterns and suppresses isolated pulses better than the mean filter. Figure 1.26 illustrates the effect of using both the median and mean filter on step edge and double pulse patterns in a one-dimension example, using a filter size of 5. It is clear that the results obtained from the median filter are more satisfactory than those produced by the mean filter. It should be noted that the larger the window size, the greater the smoothing effect, as illustrated in Figure 1.27, which shows an ERS-1 radar image of an agriculture area in Cambridgeshire in eastern England. Figures 1.27b and c are filtered images with window size of 5×5 and 9×9, respectively. Figure 1.27c is more blurred (smoothed) than Figure 1.27b.

1.8.3 Adaptive filters

In comparison with non-adaptive speckle filters, adaptive speckle filters are more successful in preserving subtle image information. A number of adaptive speckle filters have been proposed, the best known being the Lee filter (Lee, 1980, 1981, 1986), the Kuan filter (Kuan et al., 1987), the Frost filter (Frost et al., 1982), and the Refined Gamma Maximum-A-Posteriori (RGMAP) filter (Touzi et al., 1988; Lopes et al., 1990; Baraldi and Parmiggiani, 1995a). Since these filters have different levels of mathematical com

Figure 1.25 Calculation of median and mean filters for 3×3 window.

[Cover] [Contents] [Index]


Classification Methods for Remotely Sensed Data
Classification Methods for Remotely Sensed Data, Second Edition
ISBN: 1420090720
EAN: 2147483647
Year: 2001
Pages: 354

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net