The intent of the classification process is to categorize all pixels in a digital image into one of several land cover classes, or 'themes'. this categorized data may then be used to produce thematic maps of the land cover present in an image. normally, multi-spectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization . the objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground. image classification is perhaps the most important part of digital image analysis. it is very nice to have a "pretty picture" or an image, showing a magnitude of colors illustrating various features of the underlying terrain, but it is quite useless unless to know what the colors mean.