A rule-based urban land use inferring method for fine-resolution multispectral imagery

Abstract

Detailed urban land use mapping requires high-resolution remotely sensed data. The pan-sharpened multispectral IKONOS imagery of 1 m pixel resolution is experimented with for urban land use classification. With the increase of spatial resolution, between-class spectral confusion and within-class spectral variation increase. Spectral-based traditional image classification methods cannot be directly applied to the IKONOS data for urban land use mapping. In this study, a rule-based urban land use inferring method is proposed and tested on 36 samples of typical land use classes and an IKONOS subscene of various classes in London, Ontario, Canada. The proposed method includes two general steps. First, the conventional multispectral classification method is applied to produce a preliminary land cover map. Second, urban land use information is inferred from the combination of several land cover classes existing in a neighbourhood by a rule-based modelling process. The inferring rules involve the percent composition ranges of compatible land cover categories for a certain land use class, the interrelationship of the compatible land covers, and exclusion of incompatible land covers. The results show that the proposed method has successfully identified level II and level III land use classes using the U.S. Geological Survey land use classification system. The proposed method has successfully identified the land use classes in the sample image with over 90% accuracy. For the subscene, the proposed method has produced a land use map with 88.5% overall accuracy.