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Abstract
Hierarchical Feature Extraction: Removing the Clutter
Track: Remote Sensing Imagery
Author(s): Mark Mangrich, David Opitz
Current techniques for interpreting imagery and populating GIS databases are inadequate. Manual interpretation is too slow, and current image processing techniques are generally not accurate enough. Recent improvements in image interpretation utilize inductive learning algorithms. These systems show promise because they can process imagery quickly. However, objects in images are very complex. It is difficult for inductive learners to identify complex features in an image with one model. The results are often cluttered. This paper presents a system that applies a hierarchy of inductive learning algorithms that assist an analyst in interactively removing classification errors through a "data-driven" process.
Mark Mangrich
University of Montana
Computer Science Department
Social Science Building
Missoula , MT 59812
USA
Phone: 406-243-2831
Fax: 406-242-2831
E-mail: mmangric@vls-inc.com
David Opitz
University of Montana
Computer Science Department
Social Science Building
Missoula
USA
Phone: 406-243-2831
E-mail: opitz