Today many methods have been used in classifying remote sensing images. However

developing classification algorithm which is capable of processing both images and other ancillary spatial data still remains to be an active research area. In this paper

the affinity index of David W. Goodall based on probability was explained

and its application possibility in remote sensing and other spatial data integrated classification was studied. Based on Goodall’s affinity index

a computer program for classifying both remote sensing and other spatial data was developed within GRASS environment. To see the result of this program

it was tested in a case study area and compared with other popular classification methods such as maximum likelihood classification and evidential classification. Through this study

we would like to know how the other spatial data can help improve the remote sensing image classification and whether the algorithm based on Goodall’s affinity index is good in classifying remote sensing images and other ancillary spatial data in an integrate way.