Improved feature extraction from high-resolution remotely sensed imagery using object geometry

Abstract

Information extraction from high spatial resolution imagery is sometimes hampered by the limited number of spectral channels available from these systems. Standard supervised classification algorithms found in commercial software packages may misclassify different features with similar spectral characteristics; leading to a high occurrence of false positives. An additional step in the information extraction process was developed incorporating the concept of object geometry. Objects are defined as a contiguous group of pixels identified as belonging to a single class in the spectral classification. Using results from the spectral classification, a supervised approach was developed using genetic programming to select and mathematically combine feature-specific shape descriptors from a larger set of shape descriptors, to form a new classifier. This investigation focused on extraction of residential housing from QuickBird and IKONOS imagery of the Mississippi Gulf Coast before and immediately after hurricane Katrina. Use of genetic programming significantly reduced false positives caused by asphalt pavement and isolated roofing material scattered throughout the image.