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.

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 two sattelite bands, as shown in the picture.

Example plot of the brightness values of two satellite bands