Andere Kunden interessierten sich auch für
![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 Methods63,99 €![Hyperspectral Imagery Target Detection Using Principal Component Analysis]( "Hyperspectral Imagery Target Detection Using Principal Component Analysis")
Hyperspectral Imagery Target Detection Using Principal Component Analysis63,99 €![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 Algorithm63,99 €![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 Experiments64,99 €![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 Imagery63,99 €![Abstracting GIS Layers from Hyperspectral Imagery]( "Abstracting GIS Layers from Hyperspectral Imagery")
Abstracting GIS Layers from Hyperspectral Imagery64,99 €![Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance]( "Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance")
Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance63,99 €
The purpose of this research was to expand the current catalog of hyperspectral imagery (HSI) anomaly detector algorithms by proving the effectiveness of a new detector dubbed Iterative RX. Specifically, the Iterative RX detector is measured against the established HSI "benchmark" detector known as RX. Principal component analysis was employed as a data reduction technique. Design of experiments was used to ascertain the significance of three factors including algorithm used. Detector performance was measured both by the standard operating characteristic curve and by the novel clump operating characteristic curve. Matlab code for the Iterative RX algorithm, Baseline RX algorithm, and a clump grading function are provided. Furthermore, recommended algorithm settings and procedures for the application of the Iterative RX detector are also provided.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.