Knowledge-based crop classification of a Landsat Thematic Mapper image

Abstract

A knowledge-based classification method was designed to improve crop classification accuracy. Crop data of preceding years, stored in a geographical information system (GIS) were used as ancillary data. Knowledge about crop succession, determined from crop rotation schemes, was formalized by means of transition matrices. The spectral data, the data from the GIS and the knowledge represented in the transition matrix were used in a modified Bayesian classification algorithm. The developed classification was tested in an agricultural region in The Netherlands. Depending on the spectral class discrimination, the accuracy of the knowledge-based classification was 6 to 20 percent better compared with a maximum likelihood classification.