Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data

Abstract

A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio‐economic status of neighbourhoods within Accra, Ghana. Two types of object‐based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation–Impervious–Soil sub‐objects. Both approaches yielded residential land‐use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.

Acknowledgements

This research was funded partially by grant number R21 HD046612‐01 from the National Institute of Child Health and Human Development. Field support in Accra was provided by Raphael Arku and Innocent Deno. Debbie Fugate assisted with census data processing and analysis.