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![Hyperspectral Imagery Target Detection Using Principal Component Analysis]( "Hyperspectral Imagery Target Detection Using Principal Component Analysis")
Hyperspectral Imagery Target Detection Using Principal Component Analysis60,99 €![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 Experiments60,99 €![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 Methods60,99 €![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 €![Abstracting GIS Layers from Hyperspectral Imagery]( "Abstracting GIS Layers from Hyperspectral Imagery")
Abstracting GIS Layers from Hyperspectral Imagery60,99 €![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 Algorithm60,99 €![Context-Aided Tracking with Adaptive Hyperspectral Imagery]( "Context-Aided Tracking with Adaptive Hyperspectral Imagery")
Context-Aided Tracking with Adaptive Hyperspectral Imagery60,99 €
Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.