Hyperspectral Imaging
Techniques for Spectral Detection and Classification
Article
N-finder algorithm (N-FINDR) is probably one of most popular and widely used algorithms for endmember extraction in hyperspectral imagery. When it comes to practical implementation, four major obstacles need t...
Chapter
Dimensionality Reduction (DR) has found many applications in hyperspectral image processing. This book chapter investigates Projection Pursuit (PP)-based Dimensionality Reduction, (PP-DR) which includes both P...
Chapter
This chapter investigates the applicability of direct application of 3D compression techniques to hyperspectral imagery and develops PCA-based spectral/spatial compression techniques in conjunction with the vi...
Book
Chapter
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 ...
Chapter
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...
Chapter
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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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...
Chapter
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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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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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 ...
Chapter
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...
Chapter
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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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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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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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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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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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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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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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...