Remote Sensing
Theory and Applications
1. Auflage
Erschienen am 17. Juni 2024
610 Seiten
978-1-5015-2284-0 (ISBN)
Beschreibung
This book explores the world of remote sensing technology, offering comprehensive insights into its principles, data acquisition methods, advanced processing techniques, and diverse applications. It covers the basics of remote sensing such as the foundational principles and data acquisition techniques, image pre-processing, such as noise removal, radiometric corrections, and image fusion, and advanced classification techniques like machine learning algorithms including neural networks and support vector machines. Finally, it discusses disaster management and agriculture, demonstrating how remote sensing methods are revolutionizing fields such as disaster response and agricultural monitoring. Professionals, researchers, and students involved in environmental sciences, geography, urban planning, and disaster management will benefit from these topics.
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Sprache
Englisch
Verlagsort
New York
USA
Zielgruppe
Für Beruf und Forschung
US School Grade: College Graduate Student
Dateigröße
18,83 MB
ISBN-13
978-1-5015-2284-0 (9781501522840)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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1. Auflage
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Inhalt
- 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 Pléiades
- 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
