Geography Reference
In-Depth Information
Experimentally, one parameter has been selected. The mean parameter in order 3 after
several tests on our studies site has been selected. The method of choice of the index and
order of textural parameters is largely presented in (Fotsing et al., 2008).
4.3. Principle of detection modes and valleys of the histogram
4.3.1. Histogram modeling
The histogram of an image is a graphic representation having abscissa values of gray levels,
and the ordinate the number of pixels associated with each gray level value. The mode is a
local maximum and valley a local minimum of the histogram. The maximum and minimum
(no zero) of a histogram indicate a group of pixels and is used to detect cluster centers.
Kourgly et al. (Kourgly et al., 2003) exploit observed nesting on the experimental variogram
textures for segmentation urban image.
How to extract the classes contained in a SAR image? A good method for extracting
classes is that will arrive at a correct interpretation. To achieve this goal, we used
thresholding techniques. We go with the principle that, the thresholding has aim to
segment an image in to several classes using only histogram. This assumes that the
information associated with the image alone allows the segmentation, which is to say that
a class is characterized by its gray level distribution. At each peak of the histogram has an
associated class.
There are numerous methods of thresholding a histogram (Diday et al., 1982; Otsu, 1979).
Most of these methods are applied correctly if the histogram actually contains separate
peaks. Moreover, these methods have often been developed to treat the particular case of
segmentation in two classes (that is to say moving to a binary image) and generality face
multi-class case is rarely warranty. In this work, we assume that each class corresponds to a
different range of gray level. The histogram is then m-modal. The position of minima and
maxima of the histogram
can set the thresholds
−1
to separate the
classes.
In mathematical terms, the thresholds
are obtained by equations given below:
=ℎ∈
,
(10)
=ℎ∈
,
(11)
Equations (10) and (11) indicate the thresholds for valleys and modes of the histogram,
respectively. Similarly, in these expressions
and
are the mean values (modes or
valleys) of the light intensity in the classes
and
. The range
,
is obtained on
the basis of average values of the valleys, these when the threshold is calculated by the
equation (10) and by (11) otherwise.
The histogram gives comprehensive information on the distribution of gray levels in the
image. If we note
the value of gray level, another way to represent the histogram can be to
search for a mathematical expression
=
with
the number of pixels whose gray level
. The form of the function
determines the signature of the analyzed image. Based on the
