Object recognition and image segmentation: the Feature Analyst^® approach

Abstract

The collection of object-specific geospatial features, such as roads and buildings, from high-resolution earth imagery is a time-consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Traditional collection methods, such as hand-digitizing, are slow, tedious and cannot keep up with the ever-increasing volume of imagery assets. In this paper we describe the methodology underlying the Feature Analyst automated feature extraction (AFE) software, which addresses this core problem in GIS technology. Feature Analyst, a leading, commercial AFE software system, provides a suite of machine learning algorithms that learn on-the-fly how to classify object-specific features specified by an analyst. The software uses spatial context when extracting features, and provides a natural, hierarchical learning approach that iteratively improves extraction accuracy. An adaptive user interface hides the complexity of the underlying machine learning system while providing a comprehensive set of tools for feature extraction, editing and attribution. Finally, the system will automatically generate scripts that allow batch-processing of AFE models on additional sets of images to support large-volume, geospatial, data-production requirements.