Hyperspectral data processing : algorithm design and analysis /

Hyperspectral data processing : algorithm design and analysis /

"Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectra...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Chang, Chein-I
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Hoboken, NJ : John Wiley & Sons, Inc., 2013.
Θέματα:
Remote sensing.
Spectroscopic imaging.
Multispectral imaging.
High performance computing.
Image processing > Digital techniques.
Image processing > Statistics.
Signal processing.
TECHNOLOGY & ENGINEERING > Imaging Systems.
COMPUTERS > Computer Engineering.
COMPUTERS > Hardware > General.
COMPUTERS > Machine Theory.
Electronic books.
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 1 Overview and introduction
  • 2 Fundamentals of subsample and mixed sample analyses
  • 3 Three-dimentsional receiver operating characteristics (3D ROC) analysis
  • 4 Design of synthetic image experiments
  • 5 Virtual dimensionality of hyperspectral data
  • 6 Data dimensionality reduction
  • 7 Simultaneous endmember extraction algorithms (SM-EEAs)
  • 8 Sequential endmember extraction algorithms (SQ-EEAs)
  • 9 Initialization-driven endmember extraction algorithms (ID-EEAs)
  • 10 Random endmember extraction algorithms (REEAs)
  • 11 Exploration on relationships among endmember extraction algorithms
  • 12 Orthogonal subspace projection revisited
  • 13 Fisher's linear spectral mixture analysis
  • 14 Weighted abundance-constrained linear spectral mixture analysis
  • 15 Kernel-based linear spectral mixture analysis
  • 16 Hyperspectral measures
  • 17 Unsupervised linear hyperspectral mixture analyis
  • 18 Pixel extraction and information.
  • 19 Exploitation-based hyperspectral data compression
  • 20 Progressive spectral dimensionality process
  • 21 Progressive band dimentsionality process
  • 22 Dynamic dimensionality allocation
  • 23 Progressive band selection
  • 24 Binary coding for spectral signatures
  • 25 Vector coding for hyperspectral signatures
  • 26 Progressive coding for spectral signatures
  • 27 Variable-number variable-band selection for hyperspectral signals
  • 28 Kalman filter-based estimation for hyperspectral signals
  • 29 Wavelet representation for hyperspectral signals
  • 30 Application fof target detection
  • 31 Nonlinear dimensionality expansion to multispectral imagery
  • 32 Multispectral magetics resonance imaging
  • 33 Conclusions.

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