Titlebook: Hyperspectral Imaging; Techniques for Spect Chein-I Chang Book 2003 Kluwer Academic/Plenum Publishers, New York 2003 classification.detecti

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CRATE

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GORGE

Automatic Subpixel Detection: Unsupervised Subpixel Detectionextract necessary target information directly from the image data for unsupervised subpixel detection when no prior target information is available. Such generated unsupervised target information is referred to as . target information and can be also used to perform target classification as will be

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Sensitivity of Subpixel Detection One is the sensitivity of target knowledge. Another is the noise sensitivity to computation of .. required for TSCSD. Because the noise variances are determined by eigenvalues, the performance of TSCSD will be evaluated based on the number of eigenvectors used to calculate ... As will be demonstrat

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Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projectionxperiments in (1994). However, this may not be true in terms of abundance estimation. So, in this chapter, the OSP in Chapter 3 is revisited for mixed pixel classification. It is then extended by three unconstrained least-squares subspace projection approaches, called signature subspace projection (

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A Quantitative Analysis of Mixed-to-Pure Pixel Conversion (MPCV)CE data are then used to evaluate the performance of various PPC and MPC algorithms. Since the precise spatial locations of all the targets in the standardized HYDICE data are available, the candidate algorithms can be evaluated by tallying the number of targets detected and classified for quantitat

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政府

Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel ClassificationTDCA) and automatic target detection and classification algorithm (ATDCA), are presented in this chapter. The DTDCA is applied to a situation that there is knowledge about specific targets to be classified, whereas ATCDA can be used to classify targets of interest present in an unknown image scene w