Techniques and applications of hyperspectral image analysis / [edited by] Hans F. Grahn and Paul Geladi.

Format:

Book

Contributor:

Grahn, Hans.

Geladi, Paul.

Language:

English

Subjects (All):

Image processing--Statistical methods.

Image processing.

Multivariate analysis.

Multispectral imaging.

Physical Description:

1 online resource (402 p.)

Edition:

1st ed.

Place of Publication:

Chichester, England ; Hoboken, NJ : J. Wiley, c2007.

Language Note:

English

Summary:

Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more techn

Contents:

Techniques and Applications of Hyperspectral Image Analysis; Contents; 13.3 PCA; 13.3.1 History; 13.3.2 Definition; 13.3.3 Pre-processing and Scaling; 13.3.4 Noise Pre-normalization; Preface; List of Contributors; List of Abbreviations; 1 Multivariate Images, Hyperspectral Imaging: Background and Equipment; 1.1 Introduction; 1.2 Digital Images, Multivariate Images and Hyperspectral Images; 1.3 Hyperspectral Image Generation; 1.3.1 Introduction; 1.3.2 Point Scanning Imaging; 1.3.3 Line Scanning Imaging; 1.3.4 Focal Plane Scanning Imaging

1.4 Essentials of Image Analysis Connecting Scene and Variable SpacesReferences; 2 Principles of Multivariate Image Analysis (MIA) in Remote Sensing, Technology and Industry; 2.1 Introduction; 2.1.1 MIA Approach: Synopsis; 2.2 Dataset Presentation; 2.2.1 Master Dataset: Rationale; 2.2.2 Montmorency Forest, Quebec, Canada: Forestry Background; 2.3 Tools in MIA; 2.3.1 MIA Score Space Starting Point; 2.3.2 Colour-slice Contouring in Score Cross-plots: a 3-D Histogram; 2.3.3 Brushing: Relating Different Score Cross-plots; 2.3.4 Joint Normal Distribution (or Not)

2.3.5 Local Models/Local Modelling: a Link to Classification2.4 MIA Analysis Concept: Master Dataset Illustrations; 2.4.1 A New Topographic Map Analogy; 2.4.2 MIA Topographic Score Space Delineation of Single Classes; 2.4.3 MIA Delineation of End-member Mixing Classes; 2.4.4 Which to Use? When? How?; 2.4.5 Scene-space Sampling in Score Space; 2.5 Conclusions; References; 3 Clustering and Classification in Multispectral Imaging for Quality Inspection of Postharvest Products; 3.1 Introduction to Multispectral Imaging in Agriculture; 3.1.1 Measuring Quality; 3.1.2 Spectral Imaging in Agriculture

3.2 Unsupervised Classification of Multispectral Images3.2.1 Unsupervised Classification with FCM; 3.2.2 FCM Clustering; 3.2.3 cFCM Clustering; 3.2.4 csiFCM; 3.2.5 Combining Spectral and Spatial Information; 3.2.6 sgFCM Clustering; 3.3 Supervised Classification of Multispectral Images; 3.3.1 Multivariate Image Analysis for Training Set Selection; 3.3.2 FEMOS; 3.3.3 Experiment with a Multispectral Image of Pine and Spruce Wood; 3.3.4 Clustering with FEMOS Procedure; 3.4 Visualization and Coloring of Segmented Images and Graphs: Class Coloring; 3.5 Conclusions; References

4 Self-modeling Image Analysis with SIMPLISMA4.1 Introduction; 4.2 Materials and Methods; 4.2.1 FTIR Microscopy; 4.2.2 SIMS Imaging of a Mixture of Palmitic and Stearic Acids on Aluminum foil; 4.2.3 Data Analysis; 4.3 Theory; 4.4 Results and Discussion; 4.4.1 FTIR Microscopy Transmission Data of a Polymer Laminate; 4.4.2 FTIR Reflectance Data of a Mixture of Aspirin and Sugar; 4.4.3 SIMS Imaging of a Mixture of Palmitic and Stearic Acids on Aluminum Foil; 4.5 Conclusions; References; 5 Multivariate Analysis of Spectral Images Composed of Count Data; 5.1 Introduction

5.2 Example Datasets and Simulations

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on metadata supplied by the publisher and other sources.

ISBN:

9786611002084

9781281002082

1281002089

9780470010884

0470010886

9780470010877

0470010878

OCLC:

608624376