本书系统介绍了遥感的基本原理、关键技术与实际应用。全书涵盖电磁辐射与能量交互、遥感平台与传感器类型、成像机制及分辨率等基础知识，并较为全面地介绍了微波遥感的成像特性与干涉测量技术。在图像处理部分，详述了辐射校正、图像增强、频域滤波、图像融合等经典方法。后续还介绍了几何校正与正射纠正流程、基于图像的可视解译要素、多种分类算法（含深度学习方法）及精度评价方法。书中还展示了遥感技术在农业、林业、气候、生物多样性与城市发展中的典型应用，并辅以ENVI 与MATLAB 平台上的实际图像处理操作。

更多科学出版社服务，请扫码获取。

Contents
Chapter 1 Introduction to Fundamentals 1
1.1 The Role and Importance of Earth Observation 1
1.2 The Science and Process of Remote Sensing 1
1.3 Fundamental Concepts of Electromagnetic Radiation in Remote Sensing 2
1.4 Electromagnetic Energy in Remote Sensing 3
1.5 Exploring the Electromagnetic Spectrum for Remote Sensing 5
1.6 Atmospheric Radiation Dynamics 8
1.7 Interaction of Electromagnetic Radiation with Earth’s Surface 11
1.8 Sources of Energy for Remote Sensing 14
1.9 Sensing EM Energy 15
1.9.1 Sensing Characteristics 16
1.9.2 Image Characteristics 18
Exercises 19
Chapter 2 Platforms and Sensors 20
2.1 Platforms for Remote Sensing 20
2.1.1 Ground-based Sensors 20
2.1.2 Aircraft-based Sensors 20
2.1.3 Space-based Sensors 21
2.2 Sensors and Missions 22
2.2.1 Land Observation Satellites 22
2.2.2 Ocean Observation Satellites 32
2.2.3 Meteorological Observation Satellites 33
2.3 Remote Sensing Imaging Mechanism 37
2.3.1 Satellite Orbits and Remote Sensing 37
2.3.2 Cameras and Remote Sensing 40
2.3.3 Electronic Remote Sensors and Scanning Systems in Remote Sensing 43
2.3.4 The Concept, Characteristics, and Applications of Thermal Imaging 46
2.3.5 Geometric Distortion in Remote Sensing Images 47
2.3.6 Remote Sensing Data Reception, Transmission, and Processing 49
2.4 Imaging Characteristics in Remote Sensing 51
2.4.1 Understanding Spatial Resolution, Pixel Size, and Scale in Remote Sensing 51
2.4.2 Spectral Resolution in Remote Sensing 53
2.4.3 Radiometric Resolution in Remote Sensing 54
2.4.4 Temporal Resolution in Remote Sensing 54
2.5 Summary and Overview 56
Exercises 58
Chapter 3 Microwave Remote Sensing 59
3.1 Microwave Sensing: An Overview 59
3.2 Fundamentals of Radar 62
3.3 Geometry and Spatial Resolution of Radar Systems 65
3.4 Geometric Distortions in Radar Imagery 68
3.5 Target Interaction and Image Appearance in Radar 71
3.6 Radar Image Characteristics 76
3.7 Radar Topography and Interferometric Techniques 80
3.8 Airborne and Spaceborne Radar Platforms 84
3.8.1 Airborne Radar Platforms 84
3.8.2 Spaceborne Radar Platforms 86
Exercises 91
Chapter 4 Classical Analysis of RS Images 93
4.1 Visualization 93
4.1.1 Perception of Color 93
4.1.2 Image Display 98
4.2 Radiometric Corrections 100
4.2.1 Sun Elevation Correction 101
4.2.2 Haze Correction 101
4.3 Image Subsetting and Mosaicking 102
4.3.1 Image Subsetting 102
4.3.2 Image Mosaicking 104
4.4 Image Enhancement 105
4.4.1 Image Histogram 105
4.4.2 Density Slicing 105
4.4.3 Linear Enhancement 106
4.4.4 Piecewise Linear Enhancement 110
4.4.5 Look-up Table 110
4.4.6 Nonlinear Stretching 111
4.5 Spatial Filtering 112
4.5.1 Neighborhood and Connectivity 112
4.5.2 Kernels and Convolution 113
4.5.3 Image Smoothing 114
4.5.4 Median Filtering 115
4.5.5 Edge-Detection Templates 116
4.6 Multiple-Image Manipulation 117
4.6.1 Band Ratioing 117
4.6.2 Vegetation Index 118
4.7 Image Transformation 119
4.7.1 Principal Component Analysis 119
4.7.2 Tasseled Cap Transformation 121
4.7.3 IHS Transformation 122
4.8 Image Filtering in Frequency Domain 122
4.9 Image Fusion 123
Exercises 127
Chapter 5 Geometric Operations 128
5.1 Elementary Image Distortions 128
5.2 Two-dimensional Approaches 131
5.2.1 Use of Mapping Polynomials for Geometric Correction 131
5.2.2 Image-to-Image Registration and Control Point Localization 135
5.3 Three-dimensional Approaches 136
5.3.1 Orientation 138
5.3.2 Monoplotting 141
5.3.3 Orthophoto Production 142
Exercises 143
Chapter 6 Visual Interpretation 144
6.1 Introduction 144
6.2 Elements of Visual Interpretation 145
6.2.1 Basic, First Order Elements of Image Interpretation 145
6.2.2 Second Order—Geometric Arrangements of Objects 146
6.2.3 Third Order—Location or Positional Elements 149
6.3 Interpretation Key 152
6.4 The Main Steps in Image Interpretation 154
6.5 Mapping Based on Interpretation 154
6.5.1 Background Information 154
6.5.2 Image Enhancement for Visual Observation 155
6.5.3 Image Georeferencing 155
6.5.4 Data Capture and Image Interpretation 156
6.5.5 Map Composition 158
Exercises 159
Chapter 7 Image Classification 160
7.1 Classification Principle 160
7.1.1 Types 161
7.1.2 Process 163
7.2 Classification Method 164
7.2.1 Typical Supervised Classification 164
7.2.2 Object-oriented Image Classification 174
7.2.3 Unsupervised Classification 175
7.2.4 Deep Learning Based Image Classification 179
7.3 Accuracy Evaluation 186
Exercises 187
Chapter 8 Applied Remote Sensing Technologies 189
8.1 Agriculture 189
8.1.1 Crop Monitoring 189
8.1.2 Indicators for Crop Monitoring 190
8.1.3 Mapping Soil Types 192
8.1.4 Increasing Precision in Farming 193
8.2 Forestry 195
8.2.1 Estimating Forest Supplies 195
8.2.2 Monitoring Forest Fires 196
8.2.3 Preventing Spread of Forest Disease 199
8.3 Climate 200
8.3.1 Forecasting Weather 200
8.3.2 Tracking Air Quality in the Lower Atmosphere 201
8.3.3 Observing Glacier Melting and Sea Level 202
8.4 Biodiversity 204
8.4.1 Counting Polar Bears 204
8.4.2 Figuring Out Habitat Suitability for Pandas 205
8.4.3 Marine Life and Environmental Preservation 206
8.5 Other Applications 208
8.5.1 Mapping Out Ocean Floors 208
8.5.2 Extracting Mineral Deposits 209
8.5.3 Predicting Potential Landslides 210
8.5.4 Tracking Urban Growth 210
Exercises 211
Chapter 9 RS Image Processing Using ENVI 212
9.1 ENVI’s Graphical User Interface (GUI) 212
9.2 File Management in ENVI 213
9.2.1 The File Menu 213
9.2.2 Opening Image Files 214
9.2.3 Opening External Files 215
9.2.4 Available Files List 216
9.3 Display Management 217
9.3.1 Display Group 217
9.3.2 Displaying Images 219
9.4 Image Enhancement 221
9.4.1 Contrast Stretching and Quick Filtering 222
9.4.2 Filtering 223
9.4.3 Default (Quick) Stretches 223
9.4.4 Matching Histograms 224
9.5 Image Georeferencing 225
9.5.1 Ground Control Points(GCPs) 226
9.5.2 Minimizing RMS Error 229
9.5.3 Warping and Resampling 230
9.6 Unsupervised Classification 231
9.6.1 ISODATA Classification 231
9.6.2 K-means Classification 233
9.7 Supervised Classification 234
Exercises 237
Chapter 10 RS Image Processing Using MATLAB 239
10.1 Fundamentals 239
10.1.1 Desktop Basics 239
10.1.2 Matrices and Arrays 240
10.1.3 Array Indexing 244
10.1.4 Workspace Variables 245
10.1.5 Calling Functions 246
10.1.6 Scripts 247
10.1.7 Functions 247
10.1.8 Help and Documentation 248
10.2 Read and Display an Image 249
10.3 Classes and Image Types 250
10.4 Arithmetic Operations 252
10.5 Histograms 253
10.5.1 Histogram Stretching 254
10.5.2 Histogram Equalization 254
10.6 Thresholding 255
10.7 Spatial Filtering 255
10.7.1 Convolution and Correlation 256
10.7.2 Smoothing 256
10.7.3 Edge Detection 257
10.8 RS Image Classification 257
10.8.1 Spectral and Texture Features 258
10.8.2 Unsupervised Classification 259
10.8.3 Supervised Classification 260
10.9 Image Processing Using Deep Learning 263
Exercises 269
References 270