Abstract
Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster–Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user’s and producer’s accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.
Highlights
Urban fringe is the transition zone with an intermixture of urban land use and rural land use.Studying the land cover and its temporal evolution in the urban fringe area is an important way to understand the urban sprawl process
The texture and band math signatures derived from the original spectral bands help to improve the discernibility of the land cover to some degree
The intrinsic inter-class similarities and intra-class variabilities in the urban fringe land cover classification were addressed using a classification framework based on the integration of a fuzzy rough set and Dempster–Shafer (DS)-based evidence reasoning
Summary
Urban fringe is the transition zone with an intermixture of urban land use and rural land use. Studying the land cover and its temporal evolution in the urban fringe area is an important way to understand the urban sprawl process. Remote sensing, developed over recent decades, has become a time- and cost-efficient way for mapping the urban fringe area and its changes [1,2,3]. Over the past few years, quality remote-sensing imagery with high spatial, spectral and temporal resolution has become popular in the applications of the earth surface environment. The quality imagery helps improve the discernibility for the complex landscape mosaic in the urban fringe area. The classification techniques used are important for the urban fringe land cover
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