Hyperspectral imagery with moderate spatial resolution (/spl sim/30 m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatio-spectral feature extraction pipelines from training data provided via an intuitive, point-and-click interface. We describe recent work exploring geospatial feature extraction from hyperspectral imagery, and from a multi-instrument fused dataset. For hyperspectral imagery, we demonstrate our system on NASA Earth Observer 1 (EO-1) Hyperion imagery, applied to agricultural crop detection. We present an evolved pipeline, and discuss its operation. We also discuss work with multi-spectral imagery (DOE/NNSA Multispectral Thermal Imager) fused with USGS digital elevation model (DEM) data, with the application of detecting mixed conifer forest.
Evolving feature extraction algorithms for hyperspectral and fused imagery
01.01.2002
840556 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Evolving Feature Extraction Algorithms for Hyperspectral and Fused Imagery
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