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Produktbild: Image Analysis Classification

Image Analysis Classification With Algorithms for Envi/idl and Python, Third Edition

Fr. 188.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

06.06.2014

Verlag

Taylor & Francis

Seitenzahl

576

Maße (L/B/H)

24.1/0.4/3.5 cm

Gewicht

970 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-1-4665-7037-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

06.06.2014

Verlag

Taylor & Francis

Seitenzahl

576

Maße (L/B/H)

24.1/0.4/3.5 cm

Gewicht

970 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-1-4665-7037-5

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Die Leseprobe wird geladen.
  • Produktbild: Image Analysis Classification
  • Images, Arrays, and Matrices Multispectral satellite images Synthetic aperture radar images Algebra of vectors and matrices Eigenvalues and eigenvectors Singular value decomposition Finding minima and maxima Exercises Image Statistics Random variables Parameter estimation Multivariate distributions Bayes’ Theorem, likelihood and classification Hypothesis testing Ordinary linear regression Entropy and information Exercises Transformations The discrete Fourier transform The discrete wavelet transform Principal components Minimum noise fraction Spatial correlation Exercises Filters, Kernels and Fields The Convolution Theorem Linear filters Wavelets and filter banks Kernel methods Gibbs–Markov random fields Exercises Image Enhancement and Correction Lookup tables and histogram functions High-pass spatial filtering and feature extraction Panchromatic sharpening Radiometric correction of polarimetric SAR imagery Topographic correction Image–image registration Exercises Supervised Classification Part Maximizing the a posteriori probability Training data and separability Maximum likelihood classification Gaussian kernel classification Neural networks Support vector machines Exercises Supervised Classification Part Postprocessing Evaluation and comparison of classification accuracy Adaptive boosting Classification of polarimetric SAR imagery Hyperspectral image analysis Exercises Unsupervised Classification Simple cost functions Algorithms that minimize the simple cost functions Gaussian mixture clustering Including spatial information A benchmark The Kohonen self-organizing map Image segmentation Exercises Change Detection Algebraic methods Postclassification comparison Principal components analysis (PCA) Multivariate alteration detection (MAD) Decision thresholds Unsupervised change classification Change detection with polarimetric SAR imagery Radiometric normalization of multispectral imagery Exercises A Mathematical Tools B Efficient Neural Network Training Algorithms C ENVI Extensions in IDL D Python Scripts Mathematical Notation References Index