280.

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(1.53)

It can be inferred from Equation (1.53) that, in a homogeneous area where indicating that (see Equation (1.45)), the Kuan filter outputs which is the same value as that output by the Lee filter. However, in an area of high contrast, , which indicates that (see Equation (1.45)). When , the estimate becomes:

(1.54)

indicating that, in a textured area, the Kuan filter still involves average term weighted by (note that it is larger than in Equation (1.48)) to perform the estimation. By comparing Equations (1.48) and (1.54), it may be concluded that, in theory, the Lee filter is more successful than the Kuan filter in preserving texture information (Baraldi and Parmiggiani, 1995b).

1.8.3.4 The Frost filter

Based on the multiplicative noise assumption as shown in Equation (1.36), the Frost filter tries to minimise the mean square error 2 (Frost et al., 1982):

(1.55)

where is the spatial co-ordinate, and mt is called the impulse response, and is given by:

(1.56)

with

(1.57)

where K1 is a normalising constant. After some simplifications, the parameter a in Equation (1.57) is eliminated, and the final formulation is:

(1.58)

where K2 is the filter parameter, and σz and μz are obtained from local

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

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