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1　Introduction 1

1.1 What is Remote Sensing 1

1.2　Electromagnetic Radiation 2

1.3　The Electromagnetic Spectrum 3

1.4　Interactions with the Atmosphere 5

1.5 Radiation-Target Interactions 7

1.6　Passive vs.Active Sensing 10

1.7　Characteristics of Images 10

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 17

2.4 Spectral Resolution 18

2.5 Radiometric Resolution 20

2.6 Temporal Resolution 20

2.7 Cameras and Aerial Photography 21

2.8 Multispectral Scanning 23

2.9 Thermal Imaging 25

2.10 Geometric Distortion in Imagery 26

2.11 Weather Satellites/Sensors 27

2.12 Land Observation Satellites/Sensors 30

2.13 Marine Observation Satellites/Sensors 36

2.14 Other Sensors 38

2.15 Data Reception,Transmission,and Processing 39

3 Microwave Remote Sensing 41

3.1 Microvaves Introduction 41

3.2 Radar Basics 43

3.3 Viewing Geometry and Spatial Resolution 44

3.4 Radar Image Distortions 47

3.5 Target Interaction and Image Appearance 49

3.6 Radar Image Properties 52

3.7 Advanced Radar Applications 55

3.8 Radar Polarimetry 57

3.9 Airborne versus Spaceborne Radars 60

3.10 Airborne and Spaceborne Radar Systems 61

4 Image Processing 64

4.1 Introduction 64

4.2 Digital Data 66

4.3 Elements of Visual Interpretation 68

4.4 Pre-Processing 69

4.5 Image Enhancement 72

4.5.1 Gray Scale Conversion 73

4.5.2 Histogram Conversion 74

4.5.3 Colour Display of Image Data 76

4.6 lmage Transform 78

4.6.1 Operations between Images 78

4.6.2 Principal Component Analysis 78

4.6.3 Tasseled Cap Transformation 79

4.7 Spatial Filtering 81

4.8 Texture Analysis 83

4.9 lmage Correlation 83

4.10 Image Fusion 85

4.10.1 Concept of Image Fusion 86

4.10.2 Objectives of Image Fusion 86

4.10.3 Image Fusion Techniques 89

4.10.4 Examples of Image Fusion 97

4.10.5 Advantages and limitations 99

4.11 Image Classification 103

4.11.1 Classification Techniques 103

4.11.2 Estimation of Population Statistics 105

4.11.3 Clustering 106

4.11.4 Parallelepiped Classifier 107

4.11.5 Decision Tree Classifier 108

4.11.6 Minimum Distance Classifier 109

4.11.7 Maximum Likelihood Classifier 111

4.11.8 Applications of Fuzzy Set Theory 112

4.11.9 Classification Using an Expert System 113

5 Applications 115

5.1 Agriculture 116

5.1.1 Crop Type Mapping 117

5.1.2 Crop Monitoring ＆ Damage Assessment 119

5.1.3 Global Vegetation Map 121

5.2 Forestry 121

5.2.1 C1ear Cut Mapping ＆ Deforestation 124

5.2.2 Species Identification ＆ Typing 125

5.2.3 Burn Mapping 127

5.3 Hvdrology 128

5.3.1 Flood Delineation ＆ Mapping 129

5.3.2 Soil Moisture 131

5.3.3 Water Quality Monitoring 132

5.4 Land Cover ＆ Land Use 133

5.4.1 Land Cover Classification 135

5.4.2 Land Cover/Biomass Mapping 136

5.4.3 Land Cover Change Detection 137

5.5 Mapping 138

5.5.1 Planimetry 139

5.5.2 Height Measurement(DEM Generation) 140

5.5.3 Topographic ＆ Baseline Thematic Mapping 141

5.5.4 Geological Interpretation 143

References 144

Appendix A Abbreviations and acronyms 149

Appendix B Colour Figure 151