●Chapter 1 Introuduction to Fundamentals
1.1 Definition of Remote sensing
1.2 Electromagnetic Radiation
1.3 The Electromagnetic Spectrum
1.4 Interactions with the Atmosphere
1.5 Radiation-Target Interactions
1.6 Passive vs. Active Sensing
1.7 Characteristics of Images
Exercises
Chapter 2 Satellites and Sensors
2.1 On the Ground, In the Air, In Space
2.2 Satellite Characteristics: Orbits and Swaths
2.3 Spatial Resolution, Pixel Size, and Scale
2.4 Spectral Resolution
2.5 Radiometric Resolution
2.6 Temporal Resolution
2.7 Cameras and Aerial Photography
2.8 hispeetral Scanning
2.9 Thermal Imaging
2.10 Geometric Distortion in Imagery
2.11 Weather Satellites/Sensors
2.12 Land Observation Satellites/Sensors
2.13 Marine Observation Satellites/Sensors
2.14 Other Sensors
2.15 Data Reception, Transmission, and Processing
Exercises
Chapter 3 Microwave Remote Sensing
3.1 Introduction
3.2 Radar Basics
3.3 Viewing Geometry and Spatial Resolution
3.4 Radar Image Distortions
3.5 Target Interaction and Image Appearance
3.6 Radar Image Properties
3.7 Advanced Radar Applications
3.8 Radar Polarimetry
3.9 Airborne versus Spaceborne Radars
3.10 Airborne and Spaceborne Radar Systems
3.10.1 Airborne Radar Systems
3.10.2 Spaceborne Radar Systems
Chapter4 Image Interpretaion&Analysis
4.1 Introduction
4.2 Elements of Visual Interpretation
4.3 Pre-processing
4.3.1 Radiometric Corrections
4.3.2 Correction of Geometric Distortion
4.4 Image Subsetting and Mosaicking
4.4.1 Image Subsetting
4.4.2 Image Mosaicking
4.5 Image Enhancement
4.5.1 Image Histogram
4.5.2 Density Slicing
4.5.3 Linear Enhancement
4.5.4 Piecewise Linear Enhancement
4.5.5 Look-up Table
4.5.6 Nonlinear Stretching
4.6 Spatial Filtering
4.6.1 Neiorhood and Connectivity
4.6.2 Kernels and Convolution
4.6.3 Image Smoothing
4. Median Filtering
4.6.5 Edge-Detection Templates
4.7 ltiple-Image Manipulation
4.7.1 Band Ratioing
4.7.2 Vegetation Index
4.8 Image Transformation
4.8.1 PCA
4.8.2 Tasseled Cap Transformation
4.8.3 HIS Transformation
4.9 Image Filtering in Frequency Domain
4.10 Fundamentals of Classification
4.10.1 Spectral Class versus Information Class
4.10.2 Distance in the Spectral Domain
4.11Unsupervised Classification
4.11.1 Moving Cluster Analysis
4.11.2 Iterative Self-Organizing Data Analysis
4.11.3 Agglomerative Hierarchical Clustering
4.11.4 Histogram-Based Clustering
4.12Supervised Classification
4.12.1 Procedure
4.12.2 Per-Pixel Image Classifiers
4.13 Unsupervised and Supervised Classification
4.14 Other methods for classification
4.14.1 Mean Shift Clustering
4.14.2 Fuzzy Image Classification
4.14.3 Neural Network
4.14.4 Decision Tree
4.15 Data Integration and Analysis
Exercises
Chapter 5 Applications
5.1 Introduction
5.2 Land Use & Land Cover ( Rural / Urban)
5.2.1 Basic Concepts
5.2.2 Change Detection Steps
5.2.3 Common Satellite and Sensor in LULC Research
5.2.4 Case Study
5.3 Urban Thermal Environment
5.3.1 Introduction
5.3.2 Case study-Yangtze River Delta
Exercises
Chapter 6 ERDAS User' s Guide
6.1 Introduction to ERDAS
6.2 Getting Started
6.3 Viewer
Image Enhance
6.5 Image Rectification
6.6 Unsupervised Classification
6.7 Supervised Classification
1 Matrix Indeng
2 Function Imadjust
3 Logarithmic and Contrast-Stretching Transformation
4 Generating and Plotting Image Histograms
5 Linear Spatial Filtering
6 Basic Steps in DFT Filtering
7 Lowpass Frequency Domain Filters
8 Dilation and Erosion
9 Edge Detection Using Function edge
References