Abstract
Abstract. The goal of automating the process of identifying changes to topographic features in aerial photography, extracting the geometry of these features and recording the changes in a database, is yet to be fully realised. At Ordnance Survey, Britain's national mapping agency, research into the automation of these processes has been underway for several years, and is now beginning to be implemented in production systems. At the start of the processing chain is the identification of change – new buildings and roads being constructed, old structures demolished, alterations to field and vegetation boundaries and changes to inland water features. Using eCognition object-based image analysis techniques, a system has been developed to detect the changes to features. This uses four-band digital imagery (red, green, blue and near infra-red), together with a digital surface model derived by image matching, to identify all the topographic features of interest to a mapping agency. Once identified, these features are compared with those in the National Geographic Database and any potential changes are highlighted. These changes will be presented to photogrammetrists in the production area, who will rapidly assess whether or not the changes are real. If the change is accepted, they will manually capture the geometry and attributes of the feature concerned. The change detection process, although not fully automatic, cuts down the amount of time required to update the database, enabling a more efficient data collection workflow. Initial results, on the detection of changes to buildings only, showed a completeness value (proportion of the real changes that were found) of 92% and a correctness value (proportion of the changes found that were real changes) of 22%, with a time saving of around 50% when compared with the equivalent manual process. The completeness value is similar to those obtained by the manual process. Further work on the process has added vegetation, water and many other rural features to the list of features that can be detected, and the system is currently being evaluated in a production environment. In addition to this work, the research team at Ordnance Survey are working with the remote sensing (data collection) department to develop more efficient methods of DSM and DTM creation; more automated seamline-generation for the creation of orthoimage mosaics; and methods to automatically record simple building heights on buildings in the database. These are all methods that have been proven in a research environment – the challenge is to implement them within the working environment of the existing data collection process.
Highlights
1.1 MotivationOne of the major tasks of a National Mapping Organisation is to maintain the topographic database to ensure that it is as up to date as possible
At Ordnance Survey, Britain’s national mapping agency, one of the key performance targets set by Government is: “To ensure that 99.6% of significant real-world features, which are greater than six months old, are represented in Ordnance Survey’s geographic data”
Various techniques have been evaluated at Ordnance Survey in the past, including image-to-image comparison; Digital Surface Model (DSM) comparison; edge detection; and primitive object detection (Tompkinson et al 2003)
Summary
1.1 MotivationOne of the major tasks of a National Mapping Organisation is to maintain the topographic database to ensure that it is as up to date as possible. Many approaches rely on the availability of similar data from two different epochs – e.g. an image from 5 years ago and an image from today In this research, it was concluded after an initial investigation that a more practical approach would be to compare a new image with an existing topographic database – in effect to find only those changes which would entail a change (addition, deletion or modification) to a feature in the database. It was concluded after an initial investigation that a more practical approach would be to compare a new image with an existing topographic database – in effect to find only those changes which would entail a change (addition, deletion or modification) to a feature in the database This removes the need to have an archive of images, and removes any need to reconcile one type of image (e.g. a scanned true colour image) with another (e.g. a 4-band digital image). The third solution, involving an image classification followed by a comparison of the image features with the database features, proved to show the most potential and was the main method subsequently used in this research
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