Andere Kunden interessierten sich auch für
![Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments]( "Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments")
Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments61,99 €![Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods]( "Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods")
Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods62,99 €![Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery]( "Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery")
Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery62,99 €![Hyperspectral Imagery Target Detection Using Principal Component Analysis]( "Hyperspectral Imagery Target Detection Using Principal Component Analysis")
Hyperspectral Imagery Target Detection Using Principal Component Analysis62,99 €![Abstracting GIS Layers from Hyperspectral Imagery]( "Abstracting GIS Layers from Hyperspectral Imagery")
Abstracting GIS Layers from Hyperspectral Imagery61,99 €![Head tracking for 3D Audio Using a GPS-Aided MEMS IMU]( "Head tracking for 3D Audio Using a GPS-Aided MEMS IMU")
Head tracking for 3D Audio Using a GPS-Aided MEMS IMU63,99 €![Hyperspectral Imagery Target Detection Using the Iterative RX Detector]( "Hyperspectral Imagery Target Detection Using the Iterative RX Detector")
Hyperspectral Imagery Target Detection Using the Iterative RX Detector60,99 €
A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in 5% the time of the baseline { a 95% reduction in sensor acquisition time. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. The context-aided system is demonstrated in a high-fidelity tracking testbed, and reduces track identity error by 30%.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.