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Image classification is a procedure to automatically categorize all pixels in an image of a terrain into land cover classes. Normally, multispectral data are used to perform the classification of the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. This concept is dealt under thebroad subject, namely, Pattern Recognition. Spectral pattern recognition refers to the family of classification procedures that utilizes this pixel-by-pixel spectral information as the basis for automated land cover classification. Spatial pattern recognition involves the categorization of image pixels on the basis of the spatial relationship with pixels surrounding them. Image classification techniques aregrouped into two types, namely supervised and unsupervised. The classification process may also include features, such as, land surface elevation and the soil type that are not derived from the image. A pattern is thus a set of measurements on the chosen features for the individual to be classified. The classification process may therefore be considered a form of pattern recognition, that is, the identification of the pattern associated with each pixel position in an image in terms of the characteristics of the objects or on the earth's surface.
1 SUPERVISED CLASSIFICATION
A supervised classification algorithm requires a training sample for each class, that is, a collection of data points known to have come from the class of interest. Theclassification is thus based on how "close" a point to be classified is to each training sample. We shall not attempt to define the word "close" other than to say that both geometric and statistical distance measures are used in practical pattern recognition algorithms. The training samples are representative of the known classes of interest to the analyst. Classification methods that relay on use of training patterns are called supervised classification methods. The three basic steps (Fig. 6.23) involved in a typical supervised classification procedure are as follows:
(i) Training stage: The analyst identifies representative training areas and develops numerical descriptions of the spectral signatures of each land cover type of interest in the scene.
(ii) The classification stage: Each pixel in the image data set IS categorised intothe land cover class it most closely resembles. If the pixel is insufficientlysimilar to any training data set it is usually labeled 'Unknown'.
(iii) The output stage: The results may be used in a number of different ways.Three typical forms of output products are thematic maps, tables and digitaldata files which become input data for GIS. The output of image classificationbecomes input for GIS for spatial analysis of the terrain.
2 UNSUPERVISED CLASSIFICATION
Unsupervised classification algorithms do not compare .points to be classified with training data. Rather, unsupervised algorithms examine a large number of unknown data vectors and divide them into classes based on properties inherent to the data themselves. The classes that result stem from differences observed in the data. In particular, use is made of the notion that data vectors within a class should be in some sense mutually close together in the measurement space, whereas data vectors in different classes should be comparatively well separated. If the components of the data vectors represent the responses in different spectral bands, the resulting classes might be referred to as spectral classes, as opposed to information classes, which represent the ground cover types of interest to the analyst. The two types of classes described above, information classes and spectralclasses, may not exactly correspond to each other. For instance, two information classes, corn and soyabeans, may look alike spectrally. We would say that the two classes are not separable spectrally. At certain times of the growing season corn and soyabeans are not spectrally distinct while at other times they are. On the other hand a single information class may be composed of two spectral classes. Differences in planting dates or seed variety might result in the information class" corn" being reflectance differences of tasseled and untasseled corn. To be useful, a class must be of informational value and be separable from other classes in the data.
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