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Spectral sensing research for water monitoring applications and frontier science and technology for chemical, biological and radiological defense / / editors, Dwight Woolard, Janet Jensen
| Spectral sensing research for water monitoring applications and frontier science and technology for chemical, biological and radiological defense / / editors, Dwight Woolard, Janet Jensen |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (503 p.) |
| Disciplina | 574.1/9285 |
| Altri autori (Persone) |
WoolardDwight L
JensenJanet L. <1964-> |
| Collana | Selected topics in electronics and systems |
| Soggetto topico |
Spectrum analysis
Remote sensing Chemical detectors Multispectral photography Environmental monitoring Water - Pollution - Measurement Chemical terrorism - Prevention |
| ISBN |
1-282-44099-3
9786612440991 981-283-324-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Water sensing and monitoring sessions -- Frontier session. |
| Altri titoli varianti | Frontier science and technology for chemical, biological and radiological defense |
| Record Nr. | UNINA-9910828560903321 |
| Singapore ; ; Hackensack, NJ, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Techniques and applications of hyperspectral image analysis / / [edited by] Hans F. Grahn and Paul Geladi
| Techniques and applications of hyperspectral image analysis / / [edited by] Hans F. Grahn and Paul Geladi |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2007 |
| Descrizione fisica | 1 online resource (402 p.) |
| Disciplina | 621.36/7 |
| Altri autori (Persone) |
GrahnHans
GeladiPaul |
| Soggetto topico |
Image processing - Statistical methods
Multivariate analysis Multispectral photography |
| ISBN |
1-281-00208-9
9786611002084 0-470-01088-6 0-470-01087-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910143687803321 |
| Chichester, England ; ; Hoboken, NJ, : J. Wiley, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
