Assessing the errors generated from classification of remotely sensed data using spatial autocorrelation

In this paper, a method of analyzing the pattern of error when classification was done from remotely sensed data by using spatial autocorrelation analysis will be introduced. Various sites were picked (water, tree, grass, sand, and urban region) and corresponding reference data were supplied for comparison after classification. Classified images were compared to the reference data to assign white color (0) to the pixels that agree and grey to black color (1, 2, 3, 4; depending on the degree of disagreement) to the pixels that disagree. Thus black and white images (difference image) were produced and spatial autocorrelation was performed within grey and black pixels in difference images. Several methods of classification were applied including maximum likelihood, ISODATA and minimum distance to find out the most suitable classification after measuring spatial autocorrelations of difference images. Some of the important keywords are in bold case.