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Signal Theory Methods in Multispectral Remote Sensing

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ISBN-10: 047142028X

ISBN-13: 9780471420286

Edition: 2003

Authors: David A. Landgrebe

List price: $264.95
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Description:

Focusing on the fundamentals of the analysis of multispectral and hyperspectral image data from the point of view of signal processing engineering, this textbook provides an aid in the analysis of such data in an optimal fashion.
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Book details

List price: $264.95
Copyright year: 2003
Publisher: John Wiley & Sons, Incorporated
Publication date: 1/31/2003
Binding: Hardcover
Pages: 536
Size: 6.50" wide x 9.50" long x 1.30" tall
Weight: 2.090
Language: English

Preface
Introduction
Introduction and Background
The Beginning of Space Age Remote Sensing
The Fundamental Basis For Remote Sensing
The Systems View And Its Interdisciplinary Nature
The EM Spectrum and How Information Is Conveyed
The Multispectral Concept and Data Representations
Data Analysis and Partitioning Feature Space
The significance of second-order variations
Summary
The Basics for Conventional Multispectral Data
Radiation and Sensor Systems in Remote Sensing
Introduction
Radiation Teminology and Units
Planck's Law and Black Body Radiation
Solar Radiation
Atmospheric Effects
Sensor Optics
Describing Surface Reflectance
Radiation Detectors
Sorting Radiation by Wavelength
Multispectral Sensor Systems
The development of multispectral sensor systems
Summary
Pattern Recognition in Remote Sensing
The synoptic view and the volume of data
What is a pattern?
Discriminant Functions
Training the Classifier: An Iterative Approach
Training the Classifier: The Statistical Approach
Discriminant Functions: The Continuous Case
The Gaussian Case
Other Types of Classifiers
Thresholding
On The Characteristics, Value, And Validity Of The Gaussian Assumption
The Hughes Effect
Summary to this point
Evaluating The Classifier: Probability Of Error
Clustering: Unsupervised Analysis
The Nature of Multispectral Data in Feature Space
Analyzing Data: Putting the Pieces Together
An Example Analysis
Additional Details
Training a Classifier
Classifier Training Fundamentals
The Statistics Enhancement Concept
The Statistics Enhancement Implementation
Illustrations Of The Effect Of Statistics Enhancement
Robust Statistics Enhancement
Illustrative Examples Of Robust Expectation Maximation
Some Additional Comments
A Small Sample Covariance Estimation Scheme
Results for Some Examples
Hyperspectral Data Characteristics
Introduction
A Visualization Tool
Accuracy vs. Statistics Order
High-Dimensional Spaces: A Closer Look
Asymptotical first and second order statistics properties
High-dimensional implications for supervised classification
Feature Definition
Introduction
Ad Hoc and Deterministic Methods
Feature Selection
Principal Components / Karhunen-Loeve
Discriminant Analysis Feature Extraction (DAFE)
Decision Boundary Feature Extraction (DBFE)
Nonparametric Weighted Feature Extraction (NWFE)
Projection Pursuit
A Data Analysis Paradigm and Examples
A Paradigm for Multispectral and Hyperspectral Data Analysis
Example 1. A Moderate Dimension Example
A Hyperspectral Example Exploring Limits and Limitations
A Hyperspectral Example of Geologic Interest
Hyperspectral Analysis of Urban Data
Analyst Dependence and Other Analysis Factors
Summary and Directions
Hierarchical Decision Tree Classifiers
Use of Spatial Variations
Introduction
Use of Texture Measures
Further Evaluations of Texture Measures
A Fresh Look at the Problem
The Sample Classifier Concept
The Per Field Classifier
Finding the Boundaries of Fields
The Design of the ECHO Classifier
Sample Classification
Test of the Algorithm
Noise in Remote Sensing Systems
Introduction
Example: The Effects of Noise
A Noise Model
A Further Example of Noise Effects
A Signal and Noise Simulator
Multispectral Image Data Preprocessing
Introduction
Radiometric Preprocessing
Geometric Preprocessing
Goniometric Effects
An Outline of Probability Theory
Exercises
Index