Real-Time Recursive Hyperspectral Sample and Band Processing
Algorithm Architecture and Implementation
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Chapter and Conference Paper
Deep 2D Convolutional Neural Network with Deconvolution Layer for Hyperspectral Image Classification
Feature extraction and classification technology based on hyperspectral data have been a hot issue. Recently, the convolutional neural network (CNN) has attracted more attention in the field of hyperspectral i...
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Chapter and Conference Paper
Joint Kurtosis–Skewness-Based Background Smoothing for Local Hyperspectral Anomaly Detection
Anomaly detection becomes increasingly important in hyperspectral data exploitation due to the use of high spectral resolution to uncover many unknown substances which cannot be visualized or known a priori. T...
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Chapter and Conference Paper
Spectral Characteristics of Nitrogen and Phosphorus in Water
The concentration of nitrogen and phosphorus in the waters is an important indicator to affect water quality and determine the degree of water pollution. The development of hyperspectral remote sensing technol...
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Chapter and Conference Paper
Unsupervised Hyperspectral Band Selection Method Based on Low-Rank Representation
In order to reduce the spectral redundancy of hyperspectral remote sensing images and reduce the computational complexity of subsequent processing, an unsupervised hyperspectral image band selection algorithm ...
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Book
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Chapter
Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithm
Simplex volumes (SVs) have been used in the literature as a criterion for finding endmembers. A main issue that arises in finding SVs is inverting a nonsquare matrix, which involves excessive computing time in...
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Chapter
Recursive Hyperspectral Band Processing for Passive Target Detection: Anomaly Detection
Anomaly detection (AD) is studied extensively in Chaps. 5 and 14–18
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Chapter
Recursive Hyperspectral Band Processing of Orthogonal Subspace Projection
Progressive hyperspectral band processing (PHBP) processes data band by band without waiting for data to be completely collected according to the band-sequential (BSQ) format acquired by a hyperspectral imagin...
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Chapter
Recursive Band Processing of Fast Iterative Pixel Purity Index
As noted in Chap. 19, the performance of the pixel purity index (PPI) is largely determined by the number of skewers, K, to be used to calculate PPI counts for dat...
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Chapter
Recursive Hyperspectral Band Processing of Growing Simplex Volume Analysis
Recursive hyperspecrral band processing (RHBP) has shown promise in a variety of applications. For example, it provides progressive hyperspectral target detection maps (Chaps. 13–15) or progressive unmixed abu...
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Chapter
Simplex Volume Calculation
Using maximal simplex volume (SV) as an optimal criterion for finding endmembers is a common approach and has been widely adopted in the literature. However, very little work has been reported on how SV is cal...
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Chapter
Target-Specified Virtual Dimensionality for Hyperspectral Imagery
Virtual dimensionality (VD) was first envisioned and coined by Chang (Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers, New York, 2003) and later w...
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Chapter
Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detection
In Chap. 5, a particular subtarget detection technique in active hyperspectral target detection, called constrained energy minimization (CEM), was developed for its real-time and causal implementation. Rather ...
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Chapter
Recursive Hyperspectral Sample Processing of Orthogonal Subspace Projection
Orthogonal subspace projection (OSP) developed by Harsanyi and Chang (IEEE Transactions on Geoscience and Remote Sensing 32:779–785, 1994) (see Hyperspectral image: spectral techniques for detection and classi...
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Chapter
Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing Algorithm
The simplex growing algorithm (SGA) developed by Chang et al. (A growing method for simplex-based endmember extraction algorithms. IEEE Transactions on Geoscience and Remote Sensing 44(10): 2804–2819, 2006b) h...
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Chapter
Recursive Hyperspectral Band Processing for Active Target Detection: Constrained Energy Minimization
Chapter 5 extends constrained energy minimization (CEM) to a real-time processing version of CEM, called real-time CEM (RT CEM) that allows CEM to process data acc...
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Chapter
Recursive Hyperspectral Band Processing of Automatic Target Generation Process
The automatic target generation process (ATGP) presented in Sect. 4.4.2.3 has been widely used for unsupervised hyperspectral target detection. It detects targets ...
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Chapter
Recursive Hyperspectral Band Processing of Linear Spectral Mixture Analysis
In previous chapters, recursive hyperspectral band processing (RHBP) was developed for subpxiel detection, RHBP of constrained energy minimization in Chap. 13, RHB...
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Chapter
Conclusions
Writing a book has the great advantage over writing journal articles in the sense that the former can set the tone and agenda for carrying out what the author wants to deliver, as opposed to the latter, which ...
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Chapter
Introduction
With advanced remote sensing technology hyperpectral imaging has become an emerging technique that has found its way into many applications ranging from geology, agriculture, and law enforcement to defense, me...
