In traditional unsupervised classification method, the number of clusters usually needs to be assigned subjectively by
analysts, but in fact, in most situations, the prior knowledge of the research subject is difficult to acquire, so the suitable
and best cluster numbers are very difficult to define. Therefore, in this research, an effective heuristic unsupervised
classification method-Genetic Algorithm (GA) is introduced and tested here, because it can be through the
mathematical model and calculating procedure of optimization to determine the best cluster numbers and centers
automatically. Furthermore, two well-known models--Davies-Bouldin's and the K-Means algorithm, which adopted by
most research for the applications in pattern classification, are integrated with GA as the fitness functions. In a word, in
this research, a heuristic method-Genetic Algorithm (GA), is adopted and integrated with two different indices as the
fitness functions to automatically interpret the clusters of satellite images for unsupervised classification. The
classification results were compared to conventional ISODATA results, and to ground truth information derived from a
topographic map for the estimation of classification accuracy. All image-processing program is developed in MATLAB,
and the GA unsupervised classifier is tested on several image examples.
Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel.
The number of classes must be selected, but seldom is ascertainable with little information in advance. Moreover,
spectral properties of specific informational classes change seasonally for satellite imagery. The relationships between
informational classes and spectral classes are not always constant, and relationships defined for one image cannot be
extended to others. Thus, the analyst has very limited or no control over the menu of classes and their specific identities.
In this study, a Genetic Algorithm is adopted to interpret the cluster centers of an image and to reveal a suitable number
of classes to overcome the disadvantage of unsupervised classification. A Genetic Algorithm is capable of dealing with
a set of numerous data such as satellite imagery pixels. An optimization consequence of the image classification is
introduced and carried out. Through an image process program developed in Mathlab, the GA unsupervised classifier
was processed on several test images for validity and on SPOT satellite imagery. The classified SPOT image was
compared with finer aerial photographs as a ground truth for the estimation of classification accuracy.
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