Hyperspectral Imaging
Techniques for Spectral Detection and Classification
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19 Result(s)
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Book
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Chapter
Introduction
Hyperspectral imaging is a fast growing area in remote sensing. It expands and improves capability of multispectral image analysis. Two hyperspectral sensors currently in use and operated in airborne platform ...
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Chapter
Target Abundance-Constrained Subpixel Detection: Partially Constrained Least-Squares Methods
Subpixel target detection has received considerable interest in remote sensing image processing in recent years (Sabol et al., 1992). Due to significantly improved spectral resolution by recent advances of rem...
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Chapter
Automatic Subpixel Detection: Unsupervised Subpixel Detection
The subpixel detection studied in Chapters 3 and 4 requires either full or partial target knowledge. In the target abundance-constrained subpixel detection, a linear mixture model is required where the complet...
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Chapter
Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification
The automatic mixed pixel classification (AMPC) considered in this chapter is fully computer automated and can be implemented to automatically detect and classify targets with no human intervention. Like the a...
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Chapter
Estimation for Virtual Dimensionality of Hyperspecyral Imagery
Determination of intrinsic dimensionality (ID) for remotely sensed imagery is a challenging problem. According to the definition given in Fukunaga (1990, p. 280), the ID, also referred to effective dimensional...
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Chapter
Automatic Subpixel Detection: Anomaly Detection
One type of automatic target detection, unsupervised subpixel detection has been considered in Chapter 5. This chapter considers another type of automatic target detection, anomaly detection. Unlike the unsupe...
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Chapter
Target Signature-Constrained Mixed Pixel Classification (TSCMPC): Linearly Constrained Discriminant Analysis (LCDA)
Fisher’s Linear Discriminant Analysis (LDA) is a widely used technique for pattem classification and was discussed in Chapter 9. It uses Fisher’s ratio, a ratio of between-class scatter matrix to within-class ...
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Chapter
Automatic Mixed Pixel Classification (AMPC): Projection Pursuit
In Chapter 15, LSRMA made use of skewness and kurtosis to detect and classify potential targets automatically without prior target knowledge. These two criteria were a simplification of the contrast function i...
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Chapter
Sensitivity of Subpixel Detection
In Chapter 3, we considered the target abundance-constrained subpixel detection (TACSD) and evaluated three techniques, OSP, SCLS and NCLS. A major limitation of these techniques is the requirement of complete...
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Chapter
A Quantitative Analysis of Mixed-to-Pure Pixel Conversion (MPCV)
Over the past years many algorithms have been developed for multispectral and hyperspectral image classification. Due to a lack of standardized data, these algorithms have not been rigorously compared within a...
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Chapter
Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers
The target abundance-constrained mixed pixel classification that was considered in Chapter 10 imposed ASC and ANC on target abundance fractions. In this case, a linear mixture model is required and the target ...
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Chapter
Automatic mixed pixel classification (AMPC): Linear spectral random mixture analysis (LSRMA)
Independent component analysis (ICA) has shown much success in blind source separation and channel equalization. Its applications to remotely sensed images are investigated in recent years. Linear spectral mix...
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Chapter
Hyperspectral Measures for Spectral Characterization
A hyperspectral image can be considered as an image cube where the third dimension is represented by hundreds of contiguous spectral bands. As a result, a hyperspectral pixel is actually a column vector with d...
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Chapter
Target Signature-Constrained Subpixel Detection: Linearly Constrained Minimum Variance (LCMV)
In Chapter 3, we studied partially constrained least squares approaches to target abundance-constrained subpixel detection. One of their practical limitations is the requirement of prior knowledge about the ta...
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Chapter
Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projection
The orthogonal subspace projection (OSP) for hyperspectral image classification was first reported in (1994) and has been successfully applied to hyperspectral data exploitation since then. Its ability in subp...
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Chapter
Target Abundance-Constrained Mixed Pixel Classification (TACMPC)
The mixed pixel classification (MPC), which was considered in Chapters 8 and 9 is unconstrained with no constraint imposed on the target signature abundance fractions. Consequently, the resulting abundance est...
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Chapter
Automatic Mixed Pixel Classificatio (AMPC): Anomaly Classification
In Chapter 13, one type of AMPC, the unsupervised MPC, was considered. It made use of an unsupervised algorithm to generate necessary unsupervised target information required for unsupervised MPC. In this chap...
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Chapter
Conclusions and Further Techniques
As a final chapter, we will highlight several features that are considered to be unique in this book and conclude with a number of interesting techniques for which we are unable to cover in this book. The most...
