Talk:Image classification
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e.g. forest, water or settlement areas, easier to recognize. | e.g. forest, water or settlement areas, easier to recognize. | ||
| − | The basic concept is the identification of pixels with similar | + | The basic concept is the identification of pixels with similar characteristics, |
and the aggregation of these pixels to ''classes''. | and the aggregation of these pixels to ''classes''. | ||
| − | Of course, this assumed ''similarity'' | + | Of course, this assumed ''similarity'' has to be carefully defined. It is |
based on the ''spectral signature'' of the respective class, usually the | based on the ''spectral signature'' of the respective class, usually the | ||
combination of [[brightness value|brightness values]] of the pixels on different | combination of [[brightness value|brightness values]] of the pixels on different | ||
| − | bands. | + | 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'' and | ||
| + | ''supervised'' approaches. They are defined by the order in which the gathered | ||
| + | information (spectral and ground based) is used. | ||
| + | |||
===Unsupervised classification=== | ===Unsupervised classification=== | ||
Revision as of 12:23, 25 June 2013
Contents |
Article discussion
- This section is meant solely for discussion purposes.
For a pre-version of the article, see #Article draft.
- This section is meant solely for discussion purposes.
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 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 and supervised approaches. They are defined by the order in which the gathered information (spectral and ground based) is used.
