A Framework for Automated Extraction and Classification of Linear Networks

This paper presents a framework for extracting networks of linear features such as roads from imagery using an object-oriented geodata model. The proof of concept approach has resulted in the Automated Linear Feature Identification and Extraction (ALFIE) which uses a control strategy to automate the process flow. The resulting system is highly flexible, incorporating a toolkit of algorithms and imagery to extract linear features and utilizes contextual information to allow evidence of class membership to be built up from a variety of sources. The classification algorithm employs a Bayesian modelling approach. This incorporates both geometric and photometric information of which five key discriminators were identified: width, width variation, sinuosity, spectral value, and spectral value variation. This paper presents an in-depth discussion of the processes undertaken by the ALFIE system and quantitative results of the final output from the system in terms of classification accuracy and network completeness.