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Signal theory methods in multispectral remote sensing [[electronic resource] /] / David A. Landgrebe
| Signal theory methods in multispectral remote sensing [[electronic resource] /] / David A. Landgrebe |
| Autore | Landgrebe D. A |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2003 |
| Descrizione fisica | 1 online resource (530 p.) |
| Disciplina | 621.3678 |
| Collana | Wiley series in remote sensing |
| Soggetto topico |
Remote sensing
Multispectral photography Signal theory (Telecommunication) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-27329-1
9786610273294 0-470-32133-4 0-471-72125-5 0-471-72380-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
SIGNAL THEORY METHODS IN MULTISPECTRAL REMOTE SENSING; Contents; PREFACE; PART I. INTRODUCTION; CHAPTER 1. INTRODUCTION AND BACKGROUND; 1.1 THE BEGINNING OF SPACE AGE REMOTE SENSING; 1.2 THE FUNDAMENTAL BASIS FOR REMOTE SENSING; 1.3 THE SYSTEMS VIEW AND ITS INTERDISCIPLINARY NATURE; 1.4 THE EM SPECTRUM AND HOW INFORMATION IS CONVEYED; 1.5 THE MULTISPECTRAL CONCEPT AND DATA REPRESENTATIONS; 1.6 DATA ANALYSIS AND PARTITIONING FEATURE SPACE; 1.7 THE SIGNIFICANCE OF SECOND-ORDER VARIATIONS; 1.8 SUMMARY; PART II. THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA
CHAPTER 2. RADIATION AND SENSOR SYSTEMS IN REMOTE SENSING2.0 INTRODUCTION; 2.1 RADIATION TERMINOLOGY AND UNITS; 2.2 PLANCK'S LAW AND BLACK BODY RADIATION; 2.3 SOLAR RADIATION; 2.4 ATMOSPHERIC EFFECTS; 2.5 SENSOR OPTICS; 2.6 DESCRIBING SURFACE REFLECTANCE; 2.7 RADIATION DETECTORS; 2.8. SORTING RADIATION BY WAVELENGTH; 2.9 MULTISPECTRAL SENSOR SYSTEMS; 2.10 THE DEVELOPMENT OF MULTISPECTRAL SENSOR SYSTEMS; 2.11 SUMMARY; CHAPTER 3. PATTERN RECOGNITION IN REMOTE SENSING; 3.1 THE SYNOPTIC VIEW AND THE VOLUME OF DATA; 3.2 WHAT IS A PATTERN?; 3.3 DISCRIMINANT FUNCTIONS 3.4 TRAINING THE CLASSIFIER: AN ITERATIVE APPROACH3.5 TRAINING THE CLASSIFIER: THE STATISTICAL APPROACH; 3.6 DISCRIMINANT FUNCTIONS: THE CONTINUOUS CASE; 3.7 THE GAUSSIAN CASE; 3.8 OTHER TYPES OF CLASSIFIERS; 3.9 THRESHOLDING; 3.10 ON THE CHARACTERISTICS, VALUE, AND VALIDITY OF THE GAUSSIAN ASSUMPTION; 3.11 THE HUGHES EFFECT; 3.12 SUMMARY TO THIS POINT; 3.13 EVALUATING THE CLASSIFIER: PROBABILITY OF ERROR; 3.14 CLUSTERING: UNSUPERVISED ANALYSIS; 3.15 THE NATURE OF MULTISPECTRAL DATA IN FEATURE SPACE; 3.16 ANALYZING DATA: PUTTING THE PIECES TOGETHER; 3.17 AN EXAMPLE ANALYSIS PART III. ADDITIONAL DETAILSCHAPTER 4. TRAINING A CLASSIFIER; 4.1 CLASSIFIER TRAINING FUNDAMENTALS; 4.2 THE STATISTICS ENHANCEMENT CONCEPT; 4.3 THE STATISTICS ENHANCEMENT IMPLEMENTATION; 4.4 ILLUSTRATIONS OF THE EFFECT OF STATISTICS ENHANCEMENT; 4.5 ROBUST STATISTICS ENHANCEMENT; 4.6 ILLUSTRATIVE EXAMPLES OF ROBUST EXPECTATION MAXIMATION; 4.7 SOME ADDITIONAL COMMENTS; 4.8 A SMALL SAMPLE COVARIANCE ESTIMATION SCHEME; 4.9 RESULTS FOR SOME EXAMPLES; CHAPTER 5. HYPERSPECTRAL DATA CHARACTERISTICS; 5.1 INTRODUCTION; 5.2 A VISUALIZATION TOOL; 5.3 ACCURACY VS. STATISTICS ORDER 5.4 HIGH-DIMENSIONAL SPACES: A CLOSER LOOK5.5 ASYMPTOTICAL FIRST AND SECOND ORDER STATISTICS PROPERTIES; 5.6 HIGH-DIMENSIONAL IMPLICATIONS FOR SUPERVISED CLASSIFICATION; CHAPTER 6. FEATURE DEFINITION; 6.1 INTRODUCTION; 6.2 AD HOC AND DETERMINISTIC METHODS; 6.3 FEATURE SELECTION; 6.4 PRINCIPAL COMPONENTS/KARHUNEN-LOEVE; 6.5 DISCRIMINANT ANALYSIS FEATURE EXTRACTION (DAFE); 6.6 DECISION BOUNDARY FEATURE EXTRACTION (DBFE); 6.7 NONPARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE); 6.8 PROJECTION PURSUIT; CHAPTER 7. A DATA ANALYSIS PARADIGM AND EXAMPLES 7.1 A PARADIGM FOR MULTISPECTRAL AND HYPERSPECTRAL DATA ANALYSIS |
| Record Nr. | UNINA-9910146067103321 |
Landgrebe D. A
|
||
| Hoboken, N.J., : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal theory methods in multispectral remote sensing [[electronic resource] /] / David A. Landgrebe
| Signal theory methods in multispectral remote sensing [[electronic resource] /] / David A. Landgrebe |
| Autore | Landgrebe D. A |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2003 |
| Descrizione fisica | 1 online resource (530 p.) |
| Disciplina | 621.3678 |
| Collana | Wiley series in remote sensing |
| Soggetto topico |
Remote sensing
Multispectral photography Signal theory (Telecommunication) |
| ISBN |
1-280-27329-1
9786610273294 0-470-32133-4 0-471-72125-5 0-471-72380-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
SIGNAL THEORY METHODS IN MULTISPECTRAL REMOTE SENSING; Contents; PREFACE; PART I. INTRODUCTION; CHAPTER 1. INTRODUCTION AND BACKGROUND; 1.1 THE BEGINNING OF SPACE AGE REMOTE SENSING; 1.2 THE FUNDAMENTAL BASIS FOR REMOTE SENSING; 1.3 THE SYSTEMS VIEW AND ITS INTERDISCIPLINARY NATURE; 1.4 THE EM SPECTRUM AND HOW INFORMATION IS CONVEYED; 1.5 THE MULTISPECTRAL CONCEPT AND DATA REPRESENTATIONS; 1.6 DATA ANALYSIS AND PARTITIONING FEATURE SPACE; 1.7 THE SIGNIFICANCE OF SECOND-ORDER VARIATIONS; 1.8 SUMMARY; PART II. THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA
CHAPTER 2. RADIATION AND SENSOR SYSTEMS IN REMOTE SENSING2.0 INTRODUCTION; 2.1 RADIATION TERMINOLOGY AND UNITS; 2.2 PLANCK'S LAW AND BLACK BODY RADIATION; 2.3 SOLAR RADIATION; 2.4 ATMOSPHERIC EFFECTS; 2.5 SENSOR OPTICS; 2.6 DESCRIBING SURFACE REFLECTANCE; 2.7 RADIATION DETECTORS; 2.8. SORTING RADIATION BY WAVELENGTH; 2.9 MULTISPECTRAL SENSOR SYSTEMS; 2.10 THE DEVELOPMENT OF MULTISPECTRAL SENSOR SYSTEMS; 2.11 SUMMARY; CHAPTER 3. PATTERN RECOGNITION IN REMOTE SENSING; 3.1 THE SYNOPTIC VIEW AND THE VOLUME OF DATA; 3.2 WHAT IS A PATTERN?; 3.3 DISCRIMINANT FUNCTIONS 3.4 TRAINING THE CLASSIFIER: AN ITERATIVE APPROACH3.5 TRAINING THE CLASSIFIER: THE STATISTICAL APPROACH; 3.6 DISCRIMINANT FUNCTIONS: THE CONTINUOUS CASE; 3.7 THE GAUSSIAN CASE; 3.8 OTHER TYPES OF CLASSIFIERS; 3.9 THRESHOLDING; 3.10 ON THE CHARACTERISTICS, VALUE, AND VALIDITY OF THE GAUSSIAN ASSUMPTION; 3.11 THE HUGHES EFFECT; 3.12 SUMMARY TO THIS POINT; 3.13 EVALUATING THE CLASSIFIER: PROBABILITY OF ERROR; 3.14 CLUSTERING: UNSUPERVISED ANALYSIS; 3.15 THE NATURE OF MULTISPECTRAL DATA IN FEATURE SPACE; 3.16 ANALYZING DATA: PUTTING THE PIECES TOGETHER; 3.17 AN EXAMPLE ANALYSIS PART III. ADDITIONAL DETAILSCHAPTER 4. TRAINING A CLASSIFIER; 4.1 CLASSIFIER TRAINING FUNDAMENTALS; 4.2 THE STATISTICS ENHANCEMENT CONCEPT; 4.3 THE STATISTICS ENHANCEMENT IMPLEMENTATION; 4.4 ILLUSTRATIONS OF THE EFFECT OF STATISTICS ENHANCEMENT; 4.5 ROBUST STATISTICS ENHANCEMENT; 4.6 ILLUSTRATIVE EXAMPLES OF ROBUST EXPECTATION MAXIMATION; 4.7 SOME ADDITIONAL COMMENTS; 4.8 A SMALL SAMPLE COVARIANCE ESTIMATION SCHEME; 4.9 RESULTS FOR SOME EXAMPLES; CHAPTER 5. HYPERSPECTRAL DATA CHARACTERISTICS; 5.1 INTRODUCTION; 5.2 A VISUALIZATION TOOL; 5.3 ACCURACY VS. STATISTICS ORDER 5.4 HIGH-DIMENSIONAL SPACES: A CLOSER LOOK5.5 ASYMPTOTICAL FIRST AND SECOND ORDER STATISTICS PROPERTIES; 5.6 HIGH-DIMENSIONAL IMPLICATIONS FOR SUPERVISED CLASSIFICATION; CHAPTER 6. FEATURE DEFINITION; 6.1 INTRODUCTION; 6.2 AD HOC AND DETERMINISTIC METHODS; 6.3 FEATURE SELECTION; 6.4 PRINCIPAL COMPONENTS/KARHUNEN-LOEVE; 6.5 DISCRIMINANT ANALYSIS FEATURE EXTRACTION (DAFE); 6.6 DECISION BOUNDARY FEATURE EXTRACTION (DBFE); 6.7 NONPARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE); 6.8 PROJECTION PURSUIT; CHAPTER 7. A DATA ANALYSIS PARADIGM AND EXAMPLES 7.1 A PARADIGM FOR MULTISPECTRAL AND HYPERSPECTRAL DATA ANALYSIS |
| Record Nr. | UNINA-9910830861503321 |
|
Landgrebe D. A
|
||
| Hoboken, N.J., : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal theory methods in multispectral remote sensing / / David A. Landgrebe
| Signal theory methods in multispectral remote sensing / / David A. Landgrebe |
| Autore | Landgrebe D. A |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2003 |
| Descrizione fisica | 1 online resource (530 p.) |
| Disciplina | 621.3678 |
| Collana | Wiley series in remote sensing |
| Soggetto topico |
Remote sensing
Multispectral photography Signal theory (Telecommunication) |
| ISBN |
1-280-27329-1
9786610273294 0-470-32133-4 0-471-72125-5 0-471-72380-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
SIGNAL THEORY METHODS IN MULTISPECTRAL REMOTE SENSING; Contents; PREFACE; PART I. INTRODUCTION; CHAPTER 1. INTRODUCTION AND BACKGROUND; 1.1 THE BEGINNING OF SPACE AGE REMOTE SENSING; 1.2 THE FUNDAMENTAL BASIS FOR REMOTE SENSING; 1.3 THE SYSTEMS VIEW AND ITS INTERDISCIPLINARY NATURE; 1.4 THE EM SPECTRUM AND HOW INFORMATION IS CONVEYED; 1.5 THE MULTISPECTRAL CONCEPT AND DATA REPRESENTATIONS; 1.6 DATA ANALYSIS AND PARTITIONING FEATURE SPACE; 1.7 THE SIGNIFICANCE OF SECOND-ORDER VARIATIONS; 1.8 SUMMARY; PART II. THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA
CHAPTER 2. RADIATION AND SENSOR SYSTEMS IN REMOTE SENSING2.0 INTRODUCTION; 2.1 RADIATION TERMINOLOGY AND UNITS; 2.2 PLANCK'S LAW AND BLACK BODY RADIATION; 2.3 SOLAR RADIATION; 2.4 ATMOSPHERIC EFFECTS; 2.5 SENSOR OPTICS; 2.6 DESCRIBING SURFACE REFLECTANCE; 2.7 RADIATION DETECTORS; 2.8. SORTING RADIATION BY WAVELENGTH; 2.9 MULTISPECTRAL SENSOR SYSTEMS; 2.10 THE DEVELOPMENT OF MULTISPECTRAL SENSOR SYSTEMS; 2.11 SUMMARY; CHAPTER 3. PATTERN RECOGNITION IN REMOTE SENSING; 3.1 THE SYNOPTIC VIEW AND THE VOLUME OF DATA; 3.2 WHAT IS A PATTERN?; 3.3 DISCRIMINANT FUNCTIONS 3.4 TRAINING THE CLASSIFIER: AN ITERATIVE APPROACH3.5 TRAINING THE CLASSIFIER: THE STATISTICAL APPROACH; 3.6 DISCRIMINANT FUNCTIONS: THE CONTINUOUS CASE; 3.7 THE GAUSSIAN CASE; 3.8 OTHER TYPES OF CLASSIFIERS; 3.9 THRESHOLDING; 3.10 ON THE CHARACTERISTICS, VALUE, AND VALIDITY OF THE GAUSSIAN ASSUMPTION; 3.11 THE HUGHES EFFECT; 3.12 SUMMARY TO THIS POINT; 3.13 EVALUATING THE CLASSIFIER: PROBABILITY OF ERROR; 3.14 CLUSTERING: UNSUPERVISED ANALYSIS; 3.15 THE NATURE OF MULTISPECTRAL DATA IN FEATURE SPACE; 3.16 ANALYZING DATA: PUTTING THE PIECES TOGETHER; 3.17 AN EXAMPLE ANALYSIS PART III. ADDITIONAL DETAILSCHAPTER 4. TRAINING A CLASSIFIER; 4.1 CLASSIFIER TRAINING FUNDAMENTALS; 4.2 THE STATISTICS ENHANCEMENT CONCEPT; 4.3 THE STATISTICS ENHANCEMENT IMPLEMENTATION; 4.4 ILLUSTRATIONS OF THE EFFECT OF STATISTICS ENHANCEMENT; 4.5 ROBUST STATISTICS ENHANCEMENT; 4.6 ILLUSTRATIVE EXAMPLES OF ROBUST EXPECTATION MAXIMATION; 4.7 SOME ADDITIONAL COMMENTS; 4.8 A SMALL SAMPLE COVARIANCE ESTIMATION SCHEME; 4.9 RESULTS FOR SOME EXAMPLES; CHAPTER 5. HYPERSPECTRAL DATA CHARACTERISTICS; 5.1 INTRODUCTION; 5.2 A VISUALIZATION TOOL; 5.3 ACCURACY VS. STATISTICS ORDER 5.4 HIGH-DIMENSIONAL SPACES: A CLOSER LOOK5.5 ASYMPTOTICAL FIRST AND SECOND ORDER STATISTICS PROPERTIES; 5.6 HIGH-DIMENSIONAL IMPLICATIONS FOR SUPERVISED CLASSIFICATION; CHAPTER 6. FEATURE DEFINITION; 6.1 INTRODUCTION; 6.2 AD HOC AND DETERMINISTIC METHODS; 6.3 FEATURE SELECTION; 6.4 PRINCIPAL COMPONENTS/KARHUNEN-LOEVE; 6.5 DISCRIMINANT ANALYSIS FEATURE EXTRACTION (DAFE); 6.6 DECISION BOUNDARY FEATURE EXTRACTION (DBFE); 6.7 NONPARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE); 6.8 PROJECTION PURSUIT; CHAPTER 7. A DATA ANALYSIS PARADIGM AND EXAMPLES 7.1 A PARADIGM FOR MULTISPECTRAL AND HYPERSPECTRAL DATA ANALYSIS |
| Record Nr. | UNINA-9910841237503321 |
|
Landgrebe D. A
|
||
| Hoboken, N.J., : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||