Complementary methods for extracting road centerlines from IKONOS imagery

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Abstract
We present both semi-automated and automated methods for road extraction using IKONOS imagery. The automated method extracts straight-line, gridded road networks by inferring a local grid structure from initial information and then filling in missing pieces using hypothesization and verification. This can be followed by the semi-automated road tracker tool to approximate curvilinear roads and to fill in some of the remaining missing road structure. After a panchromatic texture analysis, our automated method incorporates an object-level processing phase which enables the algorithm to avoid problems arising from interference such as crosswalks and vehicles. It is limited, however, in that the logic is designed for reasoning concerning intersecting grid patterns of straight road segments. Many suburban areas are characterized by curving streets which may not be well-approximated using this automatic method. In these areas, missing content can be filled in using a semi-automated tool which tracks between user-supplied points. The semi-automated algorithm is based on measures derived from both the panchromatic and multispectral bands of IKONOS. We will discuss both of these algorithms in detail and how they fit into our overall solution strategy for road extraction. A presentation of current experimentation and test results will be followed by a discussion of advantages, shortcomings, and directions for future research and improvements.

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