The use of contextual information to improve land cover classification of digital remotely sensed data

Abstract

All land cover classifications which use remotely sensed data contain error. Where this error is assumed to conform to particular spatial patterns, then it may be possible to apply automated correction procedures. Tests were carried out on urban:non-urban classifications of four sets of Landsat data of the U.K. Confusion between roads and urban areas was reduced by adding the results of linear feature detection to the urban classification. The results were then smoothed and remaining objects below a given size were removed. Results showed that increases in accuracy were obtained which were statistically significant at the 95 per cent confidence level.