遥感与图像处理 英文PDF电子书下载

Chapter 1 Introuduction to Fundamentals 1

1.1 Definition of Remote sensing 1

1.2 Electromagnetic Radiation 1

1.3 The Electromagnetic Spectrum 3

1.4 Interactions with the Atmosphere 6

1.5 Radiation-Target Interactions 8

1.6 Passive vs.Active Sensing 11

1.7 Characteristics of Images 12

Exercises 13

Chapter 2 Satellites and Sensors 14

2.1 On the Ground,In the Air,In Space 14

2.2 Satellite Characteristics:Orbits and Swaths 15

2.3 Spatial Resolution,Pixel Size,and Scale 18

2.4 Spectral Resolution 19

2.5 Radiometric Resolution 20

2.6 Temporal Resolution 20

2.7 Cameras and Aerial Photography 22

2.8 Multispectral Scanning 24

2.9 Thermal Imaging 27

2.10 Geometric Distortion in Imagery 28

2.11 Weather Satellites/Sensors 29

2.12 Land Observation Satellites/Sensors 32

2.13 Marine Observation Satellites/Sensors 40

2 14 Other Sensors 42

2.15 Data Reception,Transmission,and Processing 42

Exercises 45

Chapter 3 Microwave Remote Sensing 45

3.1 Introduction 45

3.2 Radar Basics 47

3.3 Viewing Geometry and Spatial Resolution 50

3.4 Radar Image Distortions 52

3.5 Target Interaction and Image Appearance 54

3.6 Radar Image Properties 58

3.7 Advanced Radar Applications 61

3.8 Radar Polarimetry 62

3.9 Airborne versus Spaceborne Radars 66

3.10 Airborne and Spaceborne Radar Systems 67

3.10.1 Airborne Radar Systems 67

3.10.2 Spaceborne Radar Systems 68

Exercises 72

Chapter 4 Image Interpretation ＆ Analysis 74

4.1 Introduction 74

4.2 Elements of Visual Interpretation 75

4.3 Pre-processing 76

4.3.1 Radiometric Corrections 76

4.3.2 Correction of Geometric Distortion 78

4.4 Image Subsetting and Mosaicking 82

4.4.1 Image Subsetting 82

4.4.2 Image Mosaicking 83

4.5 Image Enhancement 84

4.5.1 Image Histogram 85

4.5.2 Density Slicing 85

4.5.3 Linear Enhancement 85

4.5.4 Piecewise Linear Enhancement 88

4.5.5 Look-up Table 89

4.5.6 Nonlinear Stretching 90

4.6 Spatial Filtering 90

4.6.1 Neighborhood and Connectivity 90

4.6.2 Kernels and Convolution 91

4.6.3 Image Smoothing 92

4.6.4 Median Filtering 93

4.6.5 Edge-Detection Templates 93

4.7 Multiple-Image Manipulation 94

4.7.1 Band Ratioing 94

4.7.2 Vegetation Index 95

4.8 Image Transformation 96

4.8.1 PCA 96

4.8.2 Tasseled Cap Transformation 96

4.8.3 HIS Transformation 97

4.9 Image Filtering in Frequency Domain 99

4.10 Fundamentals of Classification 99

4.10.1 Spectral Class versus Information Class 99

4.10.2 Distance in the Spectral Domain 100

4.11 Unsupervised Classification 100

4.11.1 Moving Cluster Analysis 101

4.11.2 Iterative Self-Organizing Data Analysis 101

4.11.3 Agglomerative Hierarchical Clustering 101

4.11.4 Histogram-Based Clustering 101

4.12 Supervised Classification 103

4.12.1 Procedure 103

4.12.2 Per-Pixel Image Classifiers 104

4.13 Unsupervised and Supervised Classification 105

4.14 Other methods for classification 106

4.14.1 Mean Shift Clustering 106

4.14.2 Fuzzy Image Classification 107

4.14.3 Neural Network 108

4.14.4 Decision Tree 108

4.15 Data Integration and Analysis 109

Exercises 111

Chapter 5 Applications 113

5.1 Introduction 113

5.2 Land Use ＆ Land Cover（Rural/Urban） 114

5.2.1 Basic Concepts 114

5.2.2 Change Detection Steps 114

5.2.3 Common Satellite and Sensor in LULC Research 116

5.2.4 Case Study 116

5.3 Urban Thermal Environment 119

5.3.1 Introduction 119

5.3.2 Case study-Yangtze River Delta 121

Exercises 124

Chapter 6 ERDAS User's Guide 125

6.1 Introduction to ERDAS 125

6.2 Getting Started 126

6.3 Viewer 127

6.4 Image Enhance 132

6.5 Image Rectification 140

6.6 Unsupervised Classification 149

6.7 Supervised Classification 151

Appendix Digital Image Processing Using MATLAB 157

1 Matrix Indexing 157

2 Function Imadjust 159

3 Logarithmic and Contrast-Stretching Transformation 160

4 Generating and Plotting Image Histograms 161

5 Linear Spatial Filtering 162

6 Basic Steps in DFT Filtering 164

7 Lowpass Frequency Domain Filters 164

8 Dilation and Erosion 165

9 Edge Detection Using Function edge 167

References 169