Cover
Title Page
Copyright Page
Contents
Preface
Abbreviations
Chapter 1 Basics of Remote Sensing
1.1 Introduction
1.2 Principles of Remote Sensing
1.3 Remote Sensing Data Acquisition
1.3.1 Energy Source
1.3.2 Energy Propagation Through the Atmosphere
1.3.3 Energy Interaction with Objects/Targets
1.3.4 Energy Received by the Sensors
1.3.5 Data Transmission, Reception, and Processing
1.3.6 Data Interpretation and Analysis
1.3.7 Applications
1.4 Types of Remote Sensing Systems
1.4.1 Optical Remote Sensing System
1.4.2 Thermal Infrared Remote Sensing System
1.4.3 Microwave Remote Sensing System
1.5 Some Technical Terms
Electromagnetic Spectrum (EMS)
Reflected energy
Absorption
Transmission
Platform
Sensor
Special band
Image
Pixel
Gray scale
Digital number (DN)
Histogram
Brightness of an image
Contrast of an image
Classification
Thematic map
1.6 Various Forms of Remote Sensing Data
1.6.1 Black and White Images
1.6.2 Multispectral Images
1.6.3 Hyperspectral Images
1.6.4 Color Composite Images
1.6.4.1 Natural color composite
1.6.4.2 False color composite (FCC)
1.7 The Multi-concept in remote sensing
1.7.1 Multistage
1.7.2 Multi-Resolution
1.7.3 Multi-Band
1.7.4 Multi-Sensors
1.7.5 Multi-Tempora
1.7.6 Multi-Direction
1.7.7 Multi Disciplinary
1.7.8 Multi-Thematic Maps
1.7.9 Multi-Uses
1.8 Advantages and Disadvantages of Remote Sensing
1.8.1 Advantages of Remote Sensing
1.8.2 Disadvantages of Remote Sensing
1.9 Approaches of Remote Sensing Data Acquisition
1.9.1 Topographic/Thematic Maps
1.9.2 Advanced Surveying Instruments
1.9.3 Global Positioning System (Gps)
1.9.4 Ground Penetrating Radar (GPR)
1.9.5 Photogrammetry
1.9.6 Unmanned Aerial Vehicle (UAV)/Drone
1.9.7 Light Detection and Ranging (LiDAR)
1.9.8 Remote Sensing Images
1.10 Sources of Remote Sensing Data
Chapter 2 Electromagnetic Radiations and Interaction with Atmosphere
2.1 Introduction
2.2 Components of EMS
2.3 Interaction of EMR with Atmosphere
2.3.1 Reflection
2.3.2 Transmission
2.3.3 Absorption
2.3.3.1 Atmospheric Windows
2.3.4 Scattering
2.3.4.1 Rayleigh Scattering
2.3.4.2 Mie Scattering
2.3.4.3 Nonselective Scattering
2.4 Black Body Radiation
2.4.1 Black Body
2.4.2 Radiation Laws
2.5 Spectral Signature of Objects
2.6 Measurement of Spectral Reflectance
2.7 Atmospheric Corrections to Remote Sensing Images
Chapter 3 Various Remote Sensing Sensors and Data Characteristics
3.1 Introduction
3.2 Image Characteristics
3.2.1 Image Acquisition
3.2.2 Bits, Bytes, and Digital Number
3.2.3 Image Representation
3.2.4 Image Formats
3.3 Image Resolutions
3.3.1 Spatial Resolution
3.3.2 Spectral Resolution
3.3.3 Radiometric Resolution
3.3.4 Temporal Resolution
3.4 Remote Sensing Sensors
3.4.1 Passive Sensors
3.4.1.1 Photographic Systems
3.4.1.2 Electro-Optic Radiometers
3.4.1.3 Passive Microwave Systems
3.4.1.4 Visible, Infrared, and Thermal Imaging Systems
3.4.2 Active Sensors
3.4.2.1 Radar (Active Microwave)
3.4.2.2 LiDAR (Active Optical)
3.4.3 Optical Sensors
3.4.3.1 Sensors in Landsats
3.4.3.2 Sensors in Spots
3.4.3.3 Sensors in IRSs
3.4.3.4 Sensors in Sentinel Systems
3.4.4 Thermal Sensors
3.4.4.1 Advanced Spaceborne Thermal Emission and Reflection Radiometer
3.4.4.2 Moderate-Resolution Imaging Spectroradiometer
3.4.4.3 Advanced Very High Resolution Radiometer
3.4.4.4 Thermal Infrared Sensor
3.4.4.5 Advanced Along Track Scanning Radiometer
3.4.5 Microwave Sensors
3.4.6 Hyperspectral Sensors
Chapter 4 Various Remote Sensing Platforms
4.1 Introduction
4.2 Types of Satellite Orbits
4.2.1 Geosynchronous Orbits
4.2.2 Sun-Synchronous Orbits
4.3 Path-Row Reference
4.4 Various Satellite Platforms
4.4.1 Low Resolution Satellites
4.4.1.1 Noaa-Avhrr Systems
4.4.1.2 Terra-Modis
4.4.2 Medium Resolution Satellites
4.4.2.1 LANDSAT Systems
4.4.2.2 SPOT System
4.4.2.3 IRS Systems
4.4.2.4 Sentinel Systems
4.4.3 High/Very High Resolution Satellites
4.4.3.1 IKONOS
4.4.3.2 QuickBird
4.4.3.3 OrbView
4.4.3.4 GeoEye
4.4.3.5 WorldView
4.4.3.6 KOMPSAT
4.4.3.7 PlΓ©iades
4.4.4 Thermal Remote Sensing Platforms
4.4.5 Microwave Remote Sensing Platforms
4.4.6 Hyperspectral Imaging Platforms
4.5 Small Satellites
4.6 Selection of Remote Sensing Images
4.6.1 Spatial Characteristics
4.6.2 Spectral Characteristics
4.6.3 Repeat Interval
4.6.4 Radiometric Characteristics
4.6.5 Image Area
4.6.6 Multi-Angle Images
4.6.7 Image Availability and its Cost
4.6.8 Cost-Effectiveness of Analysis
4.6.9 Technical Expertise
Chapter 5 Image Preprocessing Approaches
5.1 Introduction
5.2 Gray Level Thresholding
5.3 Image Enhancement
5.4 Contrast Enhancement
5.4.1 Linear Contrast Enhancement
5.4.1.1 Minimum-Maximum Linear Contrast Stretch
5.4.1.2 Percentage Linear Contrast Stretch
5.4.1.3 Piecewise Linear Contrast Stretch
5.4.2 Nonlinear Contrast Enhancement
5.4.2.1 Histogram Equalization
5.4.2.2 Logarithmic Stretching
5.4.2.3 Exponential Stretching
5.4.2.4 Power-Law Transformations
5.5 Spatial Filtering
5.5.1 Low-Pass Filters
5.5.2 High-Pass Filters
5.5.2.1 Edge Detection Filters
5.5.2.2 Directional Filters
5.5.2.3 Sharpening Filters
5.6 Noise Removal
5.7 Cloud Removal
5.8 Radiometric Corrections
5.8.1 Reflectance to Radiance Conversion
5.8.2 Atmospheric Correction Models
5.8.3 Atmospheric Haze Correction
5.9 Geometric Corrections
5.9.1 Georeferencing of Images
5.9.2 Resampling Methods
5.9.2.1 Nearest Neighbor Resampling
5.9.2.2 Bilinear Interpolation Resampling
5.9.2.3 Cubic Convolution Resampling
5.10 Image Transformations
5.10.1 Arithmetic Operations
5.10.2 Ratio Index
5.10.3 Ndvi Vegetation Index
5.10.4 Other Indices
5.10.5 Tasseled Cap Transformation
5.10.6 Principal Component Analysis
5.11 Image Fusion Approaches
5.11.1 Spatio-Spectral Fusion
5.11.2 Spatiotemporal Fusion
5.11.3 Multi-Resolution Approach
5.11.4 Wavelet Transformation
5.11.5 Brovey Transformation
5.11.6 IHS Transformation
Chapter 6 Image Classification
6.1 Introduction
6.2 Manual (Visual) Interpretation Methods
6.2.1 Visual Interpretation Elements
6.2.2 Visual Interpretation Keys
6.2.3 Visual Interpretation Aids
6.2.4 Field Data Collection and Verification
6.3 Digital Interpretation
6.3.1 Supervised Classification
6.3.1.1 Minimum Distance Classification
6.3.1.2 Maximum Likelihood Classifier
6.3.1.3 Parallelepiped Classification
6.3.2 Unsupervised Classification
6.3.2.1 K-means Method
6.3.2.2 Iterative Self-Organizing Data Analysis Technique (ISODATA)
6.4 Post Classification
6.4.1 Smoothening Filters
6.4.2 Accuracy Assessment
6.4.2.1 Error Matrix
6.4.2.2 Kappa Coefficient
Chapter 7 State-of-Art Classification Techniques
7.1 Introduction
7.2 Advanced Classification Techniques
7.2.1 Artificial Neural Network (ANN)
7.2.2 Convolutional Neural Network (CNN)
7.2.3 Recurrent Neural Network (RNN)
7.2.4 Region-Based CNN
7.2.5 Fast R-CNN
7.2.6 Faster R-CNN
7.2.7 Object-Based Image Analysis (OBIA)
7.2.8 Decision Tree (DT)
7.2.9 Extraction and Classification of Homogeneous Objects (ECHO)
7.2.10 Fuzzy Classifiers
7.2.11 Fuzzy C-Means (FCM)
7.2.12 The Possibilistic C-Means
7.2.13 K-Nearest Neighbor
7.2.14 Genetic Algorithm
7.2.15 Artificial Intelligence
7.2.16 Machine Learning Classifier
7.2.17 Deep Learning Classifiers
7.2.18 Random Forest
7.2.19 Support Vector Machine
7.2.20 Markov Random Field
7.2.21 Spectral Angle Mapper
7.2.22 Spectral Mixture Analysis
7.2.23 Texture-Based Classifiers
7.2.24 Cellular Automata
7.3 Free and Open-Source Software (Foss)
7.3.1 Apache Spark
7.3.2 Clas Lite
7.3.3 E-Foto
7.3.4 Geo Express
7.3.5 Geographic Resources Analysis Support System
7.3.6 Geoserver
7.3.7 GMT Mapping Tools
7.3.8 gvSIG
7.3.9 Image Analyzer
7.3.10 Imagej
7.3.11 Integrated Land and Water Information System
7.3.12 Interimage
7.3.13 Mapnik
7.3.14 Mapserver
7.3.15 Maptitude
7.3.16 Multispec
7.3.17 Openev
7.3.18 Openlayers
7.3.19 Open Source Software Image Map
7.3.20 Optical and Radar Federated Earth Observation Toolbox
7.3.21 Opticks
7.3.22 Polarimetric Sar Data Processing (PolSARPro)
7.2.23 Python
7.3.24 Quantum Gis
7.3.25 R
7.3.26 Sentinel Toolbox
7.3.27 SPRING
7.3.28 System for Automated Geoscientific Analyses
7.3.29 TensorFlow
7.3.30 Torch
7.3.31 Waikato Environment for Knowledge Analysis
7.4 Selection of Training Samples and Classification Algorithms
Chapter 8 Applications of Remote Sensing
8.1 Introduction
8.2 Some Useful Applications
8.2.1 Agriculture Development
8.2.2 Base/Thematic Mapping
8.2.3 Digital Terrain Mapping
8.2.4 Disaster Mitigation Planning
8.2.5 Geology and Minerals
8.2.6 Healthcare
8.2.7 Infrastructure Development and Planning
8.2.8 Land Use and Land Cover Mapping
8.2.9 Location Based Studies
8.2.10 Ocean/Coastal Studies
8.2.11 Online Mapping Services
8.2.12 Site Investigations and Planning
8.2.13 Snow and Glaciers
8.2.14 Transportation Network Mapping
8.2.15 Urban Development
8.2.16 Water Resources
8.2.17 Watershed Planning and Management
8.2.18 The 3D City Models
8.3 Conclusion
Chapter 9 Land use and Land Cover Mapping and Modeling
9.1 Introduction
9.2 Need for LULC Maps
9.3 Role of Remote Sensing
9.4 Global Lulc Datasets
9.5 LULC Change Assessment
9.6 Lulc Prediction Modeling
9.7 Case Studies
9.7.1 LULC Prediction
9.7.2 Urban Growth Prediction
9.8 Conclusion
Chapter 10 Remote Sensing Platforms for Agricultural Applications
10.1 Introduction
10.2 Potentials of Remote Sensing
10.3 Various Vegetation Indices
10.4 Modern Trends
10.5 Applications in Agriculture
10.5.1 Crop Condition Assessment
10.5.2 Crop Yield and Production Forecasting
10.5.3 Precision Agriculture
10.5.4 Crop Insurance
10.6 Case Studies
10.6.1 Crop Yield Modeling
10.6.2 Crop Classification
10.7 Conclusion
Chapter 11 Disaster Monitoring and Management Using Remote Sensing Technology
11.1 Introduction
11.2 Types of Disasters
11.3 Geospatial Data for Disasters
11.3.1 Various Satellites and Sensor Images
11.3.2 Unmanned Aerial Vehicle Images
11.3.3 Point Cloud Data
11.4 Ata Integration in GIS
11.5 Disaster Management Using Remote Sensing and GIS
11.6 Applications in Various Disasters
11.6.1 Cyclones
11.6.2 Drought
11.6.3 Earthquakes
11.6.4 Forest Fire
11.6.5 River Floods
11.6.6 Landslides
11.7 Case Study: Flood Hazard Mapping
11.8 Conclusion
Chapter 12 Remote Sensing of Snow Cover
12.1 Introduction
12.2 Spectral Characteristics of Snow
12.3 Satellites and Sensors for Snow Studies
12.4 Case Study: Snow Contamination from Hyperspectral Images
12.4.1 The Study Area and Method Used
12.4.2 Snow Grain Size Measurement
12.4.3 Spectra of Contamination in Snow
12.4.3.1 Soil Contamination
12.4.3.2 Coal Contamination
12.4.3.3 Carbon Soot Contamination
12.4.3.4 Sparse Mix Vegetation Contamination
12.4.3.5 Ash Contamination
12.4.3.6 Debris Contamination
12.4.3.7 Mixed Contamination on Snow
12.4.4 Spectral Unmixing Methods for Image Classification
12.5 Conclusion
Chapter 13 Feature/Object Extraction From Remote Sensing Algorithms
13.1 Introduction
13.2 Challenges in Object Detection Algorithms
13.3 Various Object Detection Algorithms
13.4 Case Studies
13.4.1 Extraction of Riverine Features
13.4.2 Automated Building Extraction
13.4.3 Detection of Pavement Cracks
13.5 Conclusion
Chapter 14 Applying Remote Sensing for Smart Cities
14.1 Introduction
14.2 DATA: The Foundation of Smart Cities
14.3 Key Enabling Technologies for Smart Cities
14.3.1 ICT and IoT Technology
14.3.2 Geospatial Technology
14.3.3 Sensor Technology
14.3.4 Artificial Intelligence Technology
14.3.5 Blockchain Technology
14.4 Case Studies
14.4.1 Extraction of Urban Area Using Deep Learning
14.4.2 Mapping Urban Dynamics
14.5 Conclusion
Chapter 15 The Future of Remote Sensing
15.1 Introduction
15.2 Future Applications
15.3 Challenges and Problems
15.4 Opportunities
15.5 Technological Developments
15.6 Global Market Potential
15.7 Conclusion
Index