Andere Kunden interessierten sich auch für
![Improved Feature Extraction, Feature Selection, and Identification Techniques that Create a Fast Unsupervised Hyperspectral Target Detection Algorithm]( "Improved Feature Extraction, Feature Selection, and Identification Techniques that Create a Fast Unsupervised Hyperspectral Target Detection Algorithm")
Improved Feature Extraction, Feature Selection, and Identification Techniques that Create a Fast Unsupervised Hyperspectral Target Detection Algorithm59,99 €![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 Methods59,99 €![Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection Algorithms]( "Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection Algorithms")
Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection Algorithms59,99 €![Neural Extensions to Robust Parameter Design]( "Neural Extensions to Robust Parameter Design")
Neural Extensions to Robust Parameter Design59,99 €![New Tracking Filter Algorithm Using Input Parameter Estimation]( "New Tracking Filter Algorithm Using Input Parameter Estimation")
New Tracking Filter Algorithm Using Input Parameter Estimation59,99 €![Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria]( "Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria")
Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria59,99 €![Hyperspectral Imagery Target Detection Using Principal Component Analysis]( "Hyperspectral Imagery Target Detection Using Principal Component Analysis")
Hyperspectral Imagery Target Detection Using Principal Component Analysis59,99 €
Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insuciently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared for overall e ectiveness. The results of the test as well as optimal parameters for AutoGAD are presented and future research e orts proposed.