(1999) Computer Processing of Remotely-Sensed Images, An Itroduction, 2nd ed. In an image with high separability unsupervised classification may be used, whereas low separability will need the aid of supervision. To compare the unsupervised and supervised classification above is difficult, because their signature files do not show the same classes. The incorrect classification of water bodies in the Parallelepiped is due to the method’s inherent inaccuracy as the minimum and maximum value in a class seldom are representative values for a particular class. Choosing better training areas can improve this, but not necessarily so. Still, both methods display apparent errors.
The Maximum Likelihood classification above has determined more water bodies and identified dense urban areas better than the Minimum Distance. The Parallelepiped method is a rectangle, where the lowest and highest pixel values for the class in each band make up the boundary. Consequently, pixels are grouped according to their position in the influence zone of the class ellipsoid. The Maximum Likelihood classifier applies the rule that the geometrical shape of a set of pixels belonging to a class often can be described by an ellipsoid. The Minimum Distance algorithm allocates each cell by its minimum Euclidian distance to the respective centroid for that group of pixels, which is similar to Thiessen polygons. The classification algorithms will sent “sort” the pixels in the image accordingly. All methods start with establishing training samples, which are areas that are assumed or verified to be of a particular type. Supervised classification requires a priori knowledge of the number of classes, as well as knowledge concerning statistical aspects of the classes. There are different approaches to supervised classification. This is due to their low reflectance, making it difficult to distinguish between them. What is a bit puzzling is the fact that small water bodies like rivers have no distinct class, as they seem to mingle with other classes, especially roads and railways. The same goes for class 2 (roads) and 4 (railways).
Class 1 (city centre) and class 5 (residential) show the lowest average separability. Looking at the separability table, some class are clearly separable, others not so clearly. Parkland areas were consistent with park areas on the map. Agricultural areas were named so because of their typical pattern. Classĭry grassland was inferred from the area around the racecourse.
Forest, which one would expect to stand out in summer, when this picture is taken, does not have its own class. Still, not all classes are consistent, i.e. The classes were determined by referring to the Ordnance Survey Landranger map of Leicester. The convergence describes how many of the pixels stay in the same cluster between one iteration and the next. The table below summarises the convergence for every iteration, depending on the number of classes. The difference between 6 and 10 unsupervised classes is the merger of urban and residential as well as agricultural fields. Too many, and the image will not differ noticeable from the original, too few and the selection will be too coarse. When performing an unsupervised classification it is necessary to find the right number of classes that are to be found.