Object recognition and image segmentation: the Feature Analyst ® approach
3.5 Cleanup and Attribution Tools
Object-recognition and feature extraction represent steps in a chain of process used in geospatial data production to collect features for a GIS da- tabase. Other steps in the process after feature extraction include feature editing (clean-up), feature generalization, feature attribution, quality con- trol checks and then storage in the GIS database. In almost every instance of feature collection, the designated object needs to be stored as a vector feature to support GIS mapping and spatial analyses. Vector features, commonly stored in Shapefile format, can be stored as points, lines, poly- gons or TINs. One of the defining characteristics of geospatial vector fea- ture data is the ability to define topology, store feature attributes and retain geopositional information.
Feature Analyst provides tools for the majority of these tasks, with an emphasis on semi-automated and automated vector clean-up tools and fea- ture attribution. Feature representation of a road network requires that the road feature be collected as either a polygon or line feature or both. In ei- ther case, tools are required to adjust extracted vector features to account for gaps due to occlusion from overhanging trees, to eliminate dangles or overshoots into driveways, to fix intersections and to assist with a host of other tasks. As object-recognition systems evolve there is an ever- increasing expectation on the part of the user for a complete solution to the feature extraction problem.
5 Conclusions
Feature Analyst provides a comprehensive machine learning based system for assisted and automated feature extraction using earth imagery in com- mercial GIS, image processing and photogrammetry software. The AFE workflow, integrated with the supporting application tools and capabilities, provides a more holistic solution for geospatial data production tasks. The Feature Analyst user interface supports a simple feature extraction work- flow whereby the user provides the system with a set of labeled examples (training set) and then corrects the predicted features of the learning algo- rithm during the clutter removal process (hierarchical learning). Benefits of this design include:
• Significant time-savings in the extraction of 2-D and 3-D geo- spatial features from imagery. O’Brien (2003) from the National Geospatial-Intelligence Agency (NGA) conducted a detailed study that indicated Feature Analyst is 5 to 10 times faster than manual extraction methods and more accurate than hand-digitizing on most features (Fig. 8).
Fig. 8. NGA AFE test & evaluation program timing comparisons (O’Brien, 2003)
• Significant increases in accuracy. Feature Analyst has been shown to be more accurate than previous AFE methods and more accurate than hand digitizing on numerous datasets (Brewer et al 2005, O’Brien 2003).
• Workflow extension capabilities to established software. Ana- lysts can leverage Feature Analyst within their preferred workflow on their existing ArcGIS, ERDAS IMAGINE, SOCET SET and soon Remote View systems, increasing operator efficiency and output.
• A simple One-Button approach for extracting features using the Feature Model Library, as well as advanced tools for creation of geospecific features from high resolution MSI, radar, LiDAR and hyperspectral data.
• Open and standards-based software architecture allowing third-party developers to incorporate innovative feature extraction algorithms and tools directly into Feature Analyst.
0 10 20 30 40 50 60
VLS excl. proc VLS Total Manual
Extraction Time (minutes)
• Interoperability amongst users on different platforms. Expert analysts can create and store AFE models in the Feature Model Library, while other analyst can use these models for easy one- button extractions.
• A simple workflow and user interface hides the complexity of the AFE approaches.
• High accuracy with state-of-the-art learning algorithms for object recognition and feature extraction.
• Post-processing cleanup tools for editing and generalizing fea- tures, to providing an end-to-end solution for geospatial data pro- duction.
• AFE modeling tools for capturing workflows and automating fea- ture collection tasks.
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