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extraction; Using the variational spectrum knowledge: it uses
the various normalization indices mainly, and these indices
adopt the ratio transformation and the normalization treatment
mainly, and the values are between -1 and +1. It makes ground
feature get the maximal brightness enhancement on the index
image, and the other background ground features get the
common restraint. The indices in common use are vegetation
index, wave index , building index etc.
The method of discovering the spectrum knowledge includes
typical sampling method, spectrum curve method and spectrum
section plane method[3]. The information extraction based on
spectrum knowledge, need to be diacritical between ground
feature and background on the spectrum, and there is less same
spectrum phenomenon between ground feature and background,
and the internal spectrum of ground feature should be consistent.
It can carry out extraction according to the ground feature
internal characteristic component spectrum when the internal
spectrum of the ground feature disaccords. It must carry out
extraction according to the other knowledge of the ground
feature when there is more same spectrum phenomenon
between the ground feature’s internal characteristic component
spectrum and the background’s.
2.2 Remote Sensing Image Information Extraction Based
on Texture Knowledge
The image texture has reflected the character and spatial
relation of the image grayscale. The texture is a kind of spatial
distribution that the adjoining pixel gradation obeys some kind
of statistical array in the certain image area. The texture
includes the structure texture and the non-structure texture.
When the ground features’ composing are complicated and they
are bigger than the sensor’s spatial resolution, their structure
and the composing may be sensed remotely, and there is the
obvious texture characteristic on the image. When it is difficult
to resolve information extraction’s problem completely
depending on spectrum knowledge, and when there is texture
structure characteristic of differentiating the background, it
must together use the ground feature’s spectrum knowledge and
texture knowledge in information extraction.
The method of discovering texture knowledge includes gray
level co-occurrence matrix method, half variation function
method, fractal and fractal dimension method, Markov random
field and wavelet transform method, extremum texture method,
structural unit method etc, gray level co-occurrence matrix
method and half variation function method are in common use
among them.
Gray Level Co-occurrence Matrix: It’s definition is that if one
area of an image have N gray scales, the gray level co
occurrence matrix of the corresponding area is a N X N matrix,
and the definition of the pixel on (i, j) is that the pixel who’s
grayscale is i moved a given distance (displacement d) meets
the pixel who’s grayscale is j, the probability this condition
appears is p (i , j) [3] . It can calculate 13 kinds of texture
measures from the gray level co-occurrence matrix, they are: 1)
Angular Second Moment (Energy); 2) Contrast; 3) Correlation;
4) Variance; 5) Homogeneity; 6) Mean; 7) Standard Deviation;
8) Entropy; 9) Dissimilarity.
Half Variation Function: It is the geostatistics fundamental
implement. It can look remote sensing image’s grayscale as a
regionalization variable, and every pixel can be considered as
the realization of regionalization variable, it has not only
randomness but also spatial structure. According to the
definition of half variation function, the value of remote sensing
image’s half variation function reflects both the pixels’
structure and the image data’s statistics characteristic at the
certain degree, and this is the demand of describing texture
characteristic method, therefore it is reasonable to describe
remote sensing image texture with half variation function [4] .
Half variation function category: 1) direct half variation
function; 2) absolute half variation function; 3) alternate half
variation function; 4) false alternate half variation function; 5)
weighting half variation function
2.3 Remote Sensing Image Information Extraction Based
on Shape Knowledge
It is not only same or close on the spectrum characteristic but
also similar on the texture characteristic sometimes between the
ground feature and the background. It carries out information
extraction according to the ground feature’s shape knowledge in
this condition.
2.3.1 Remote Sensing Image Information Extraction
Based on Area Shape Knowledge: The ground feature
displays the certain shape on the remote sensing image. The top
or side shape of the ground feature is showed on the image, the
ground feature has the proper shape generally, for example, the
road and the river are line-type, the building is regular etc. The
ground feature also displays the certain size on the image, so it
can differentiate according to the size for two kinds of ground
features form which are difficult to differentiate owing to the
similar shape.
The factors which express the shape knowledge are the area, the
perimeter, the length and the width. The methods of discovering
the ground feature’s shape knowledge are the method based on
the perimeter and area, the method based on the area and the
method based on the area and length of the region.
The method based on the perimeter and area. E.g.
k=4aTp (d
The method based on the area. Compactness index. E.g.
C 4 =J7TA c ,C i =A/A c (2)
The method based on the area and length of the region. Aspect
Ratio. E.g.
FR = Al L 1 . (3)
Ellipse index. E.g.
EI = [tt(ì/2L)]L/A. (4)
where A = the area of the ground object
