Talk:Image classification

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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.

The desired result of a classification is (1) a thematic map of the target area and (2) information on the accuracy of the map. ^[1]

Unsupervised classification

Supervised classification

Image classification in forestry

Related articles

References

1. ↑ Wilkie, D. S. Remote sensing imagery for natural resources monitoring: a guide for first-time users. (Columbia Univ. Press, c1996).