Genetic algorithm for unsupervised classification of remote sensing imagery

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.