View source for Talk:Image classification

==Article discussion==
::''This section is meant solely for discussion purposes.<br> For a pre-version of the article, see [[#Article draft]].''

==Article draft==
'''Image classification''' covers a group of methods used to convert
remotely sensed images in a manner that makes different ''thematic classes'', 
e.g. forest, water or settlement areas, easier to recognize.

The basic concept is the identification of pixels with similar characteristics,
and the aggregation of these pixels to ''classes''.
Of course, this assumed ''similarity'' has to be carefully defined. It is
based on the ''spectral signature'' of the respective class, usually the 
combination of [[brightness value|brightness values]] of the pixels on different
bands. For a correct classification, ancillary data is needed to establish
the different classes and their thematic meaning. This can be derived from ground
information, or simply the researchers' knowledge of the terrain. 
The classification methods may be divided into [[#Unsupervised classification|unsupervised]] and
[[#Supervised classification|supervised]] approaches. They are defined by the order in which the gathered
information (spectral and ground based) is used. 

The desired result of a classification is (1) a ''thematic map'' of the
target area and (2) information on the ''accuracy'' of the map.
<ref name=Wilkie96>Wilkie, D. S. Remote sensing imagery for natural resources monitoring: a guide for first-time users. (Columbia Univ. Press, c1996).</ref>

===Unsupervised classification===
Unsupervised classification is based on so called ''clustering'' algorithms,
which assign pixels to classes by statistical means. The classes are not defined
beforehand, they are established in the clustering process, hence the
label ''unsupervised''. After the algorithm finishes the job, the researcher
has to label the output classes according to their thematic content.

Clustering algorithms basically all work the same way: they compare
the spectral similarity of pixels and, based on this information,
aggregate them into groups. Imagine the brightness values of
two sattelite bands, as shown in figure A. 
As you may know from the construction of [[color composites]], objects
on the earth's surface reflect differently in the different spectral intervals.
E.g. vegetation may reflect strongly in the near infrared spectrum and 
little in the visible red spectrum. This leads to different correlations between
the brightness values of different spectral bands of the same pixel.
Thus, if brightness values are plotted against each other, more or less
discrete groups become visible. These correlations make up the ''spectral signature''
of the pixels. In theory, the algorithm will recognize these
signatures and aggregate the pixels accordingly. In practice, pixels rarely
show such discrete spectral patterns (see figure B), which is why (1) more than two bands have to 
be used for classification and (2) the clustering process is subject to a 
more or less high error. Also, note that the spectral signature strongly depends
on external factors, such as weather, atmospheric condition, human disturbance (farming,
forest management...) or vegetation phenology (leaf production, flowering...).

<gallery widths="400" heights="400">
Image:Landsat corr fake.png|'''A:'''Example plot of the brightness values of two satellite bands
Image:Landsat corr.png|'''B:'''Brightness values of LANDSAT bands 4 and 5
</gallery>

====Iterative clustering====
Iterative clustering, also known as ''migrating means'' requires a certain number of classes to be set beforehand. For every class, a point is set at a random position in the n-dimensional space (one dimension for each band, s.o.). Afterwards, each pixel value is assigned to the nearest point, so that classes are created. Then the mean of all classes are calculated and all pixels are assigned to the nearest mean. 
This procedure is repeated, until the overall distances of points to the respective means cannot be reduced anymore. All in all, this is an application of the [[:wikipedia:Least_squares|least squares method]] also applied in linear regression analysis.

[[Image:Iterative_Clustering.png|center|thumb|1000px|An example of three iterative clustering steps for two classes. Note that the steps are not limited to three, but carried out until the distance of points to class means is minimized.]]

====Hierarchical clustering====
Hierarchical clustering algorithms start with the assumption that every pixel is an own class.
Then, with each iteration, pixels are assigned to the pixels nearest to them, thus creating
a new class.

[[Image:Hierarchical_Clustering.png|right|thumb|800px|An illustration of hierarchical clustering. The left figure shows the data points, the right figure shows a dendrogram produced by hierarchical clustering. The two main branches can be interpreted as two classes, branches of higher order may be used if more classes are desired.]]

===Supervised classification===



===Image classification in forestry===


===Related articles===


===References===
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