Decision net, directed graph, and neural net processing of imaging spectrometer dataA decision-net solution involving a novel hierarchical classifier and a set of multiple directed graphs, as well as a neural-net solution, are respectively presented for large-class problem and mixture problem treatments of imaging spectrometer data. The clustering method for hierarchical classifier design, when used with multiple directed graphs, yields an efficient decision net. New directed-graph rules for reducing local maxima as well as the number of perturbations required, and the new starting-node rules for extending the reachability and reducing the search time of the graphs, are noted to yield superior results, as indicated by an illustrative 500-class imaging spectrometer problem.
Document ID
19900024637
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Casasent, David (Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Liu, Shiaw-Dong (Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Yoneyama, Hideyuki (Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Barnard, Etienne (Carnegie-Mellon University Pittsburgh, PA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1989
Subject Category
Cybernetics
Meeting Information
Meeting: Sensor Fusion: Spatial Reasoning and Scene Interpretation