Abstract

Research in urban remote sensing has been recently reinvigorated by both the continuing fusion with GIS and the advent of high spatial resolution satellite sensor data. Both will be examined by this paper in terms of how GIS data at the point level can assist the identification and interpretation of urban land use patterns from classified land cover. Specifically, how spatial statistics can be used to summarise the two-dimensional patterns of point data representing residential and commercial buildings. In this paper point data refer to the location of postal addresses known as ADDRESS-POINT^TM and collected by the Ordnance Survey of Great Britain and COMPAS^TM in Northern Ireland. Groups of these postal points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery at the 4 m spatial resolution. In addition, the paper will outline an agenda for constructing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Preliminary results so far are most encouraging. Using ground truth from aerial photographs at 15 cm spatial resolution, classified IKONOS imagery representing two cities in the United Kingdom, Bristol and Belfast, have been investigated. In both, spatial patterns have demonstrated the ability to identify misclassified urban pixels and characterise a variety of building arrangements. Also, using the software e-Cognition, a spatial classification based on nearest neighbour contextual rules produced accuracies of 95.4% compared to 90.7% from a multispectral-only classification. Further, more extensive testing is continuing.

Keywords: Urban remote sensing; IKONOS; Nearest-neighbour; Postal point data; Land use

Article Outline

1. Introduction

1.1. Remote sensing of urban areas

2. Postal point patterns

2.1. Disaggregated geographies of Bristol and Belfast

2.2. Nearest-neighbour indices of point distributions

3. Experimental investigations

4. Conclusions

Acknowledgements

References