Remote Sensing - a tool for environmental observation

Neighbouring pixels to assign classes to pixels and

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neighbouring pixels to assign classes to pixels and are referred to as context classifiers or textural classifiers. A basic distinction between classifiers separates supervised classification from unsupervised classification: In supervised classification (figure 5.16), the image analyst controls the pixel categorization process by specifying, to the classification algorithm, numerical descriptors of the various land cover types in an image. Representative sample sites of known cover type (called training areas or ground truth polygons ) are used to characterise land cover types in terms of average
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79 reflectance values per spectral band and their variance. While classifying, each pixel in the image, is compared numerically to each category of land cover and labelled with the category, it is most similar. The success of the supervised classification depends on the capability of the analyst to define representative training areas. Some criteria for training area are: - the number of pixel per land cover type must be sufficient e.g. 100 pixels per land cover type; - the size of the training area should be sufficient large to include the spectral variance; - the training areas should be uniform with a statistically normal distribution and without outliers: the histogram of a training area should never display two or more distinct peaks, the classification can never be successful with such a histogram shape. - Figure 5.16 Principle of supervised image classification (Lillesand & Kiefer, 1994). In the unsupervised classification approach the image data are first classified by aggregating them into spectral clusters based on the statistical properties of the pixel values (average, variation). Then the image analyst determines the land cover identity of each cluster by comparing the classified image data to ground reference data. A disadvantage of the unsupervised approach is that it is often not easy to relate image clusters to land cover types. When the training stage of the supervised classification approach is completed, the image classification itself can be performed. In this classification stage the results of training are extrapolated over the entire scene. There are three widely-used classification methods: - the minimum distance to mean classifier; - the parallelepiped classifier; - the maximum likelihood classifier. The minimum distance to mean classifier is the simplest method and requires not as much computation time as the other two approaches. Figure 5.17 shows the procedure for only two
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80 spectral bands. First, the mean of each training class is calculated for each waveband (this is called the mean vector). Second, the pixels to be classified in the entire image are assigned to the class nearest to them. Third (optional), a data boundary is located at a certain distance so that if a pixel falls outside this boundary, it will be classified as unknown. The limitation of this classifier is its insensitivity to variance in the spectral properties of the classes.
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