This textbook goes beyond the traditional survey to focus on the fundamentals of the analysis of multispectral and hyperspectral image data from the point of view of signal processing engineering. It prepares both the practitioner and the student not only to analyse such data in an optimal fashion, but to be able to astutely and critically judge the value and the validity of the material which appears in the literature.
This textbook goes beyond the traditional survey to focus on the fundamentals of the analysis of multispectral and hyperspectral image data from the point of view of signal processing engineering. It prepares both the practitioner and the student not only to analyse such data in an optimal fashion, but to be able to astutely and critically judge the value and the validity of the material which appears in the literature. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
DAVID A. LANDGREBE, PhD, is Professor Emeritus of Electrical Computer Engineering in the School of Electrical and Computer Engineering at Purdue University. Dr. Landgrebe is a former president of the IEEE Geoscience and Remote Sensing Society and recipient of the Society?s Distinguished Achievement Award. He is the coauthor of Remote Sensing: The Quantitative Approach and a contributor to numerous other publications.
Inhaltsangabe
Preface. PART I: INTRODUCTION. Chapter 1. Introduction and Background. PART II: THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA. Chapter 2. Radiation and Sensor Systems in Remote Sensing. Chapter 3. Pattern Recognition in Remote Sensing. PART III: ADDITIONAL DETAILS. Chapter 4. Training a Classifier. Chapter 5. Hyperspectral Data Characteristics. Chapter 6. Feature Definition. Chapter 7. A Data Analysis Paradigm and Examples. Chapter 8. Use of Spatial Variations. Chapter 9. Noise in Remote Sensing Systems. Chapter 10. Multispectral Image Data Preprocessing. Appendix. An Outline of Probability Theory. Exercises. Index.
Preface. PART I: INTRODUCTION. Chapter 1. Introduction and Background. PART II: THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA. Chapter 2. Radiation and Sensor Systems in Remote Sensing. Chapter 3. Pattern Recognition in Remote Sensing. PART III: ADDITIONAL DETAILS. Chapter 4. Training a Classifier. Chapter 5. Hyperspectral Data Characteristics. Chapter 6. Feature Definition. Chapter 7. A Data Analysis Paradigm and Examples. Chapter 8. Use of Spatial Variations. Chapter 9. Noise in Remote Sensing Systems. Chapter 10. Multispectral Image Data Preprocessing. Appendix. An Outline of Probability Theory. Exercises. Index.
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