RADAR : Remote Sensing Data Analysis with Artificial Intelligence / Alessandro Vinciarelli, Sartajvir Singh, Narayan Vyas and Mona Abdelbaset Sadek Ali (Eds.).

Format:

Book

Contributor:

Vinciarelli, Alessandro, editor.

Singh, Sartajvir, editor.

Vyas, Narayan, 1998- editor.

Ali, Mona Abdelbaset Sadek, editor.

Language:

English

Subjects (All):

Artificial intelligence.

Radar--Data processing.

Radar.

Databases.

Information technology.

Physical Description:

1 online resource (viii, 250 pages) : illustrations, maps, charts

Edition:

1st ed.

Place of Publication:

Berlin ; Boston : De Gruyter, [2025]

Summary:

The integration of Radio Detection and Ranging (RADAR) remote sensing and Artificial Intelligence (AI) provides a platform for understanding various Earth's surface processes and their predictive analysis. This book offers state-of-the-art techniques and applications to address real-time challenges through AI-based RADAR remote sensing. Furthermore, it explores the potential applications of AI in emerging areas of remote sensing and image processing.

Contents:

00_Vinciarelli_1125_EF_Prelims

Preface

Contents

01_Vinciarelli_1125_EF_Ch01

Integrating Sentinel-1 satellite data with machine learning for land use classification

1 Introduction

2 Study area

3 Satellite data

4 Methodology

4.1 Preprocessing

4.2 Classification

5 Results

6 Conclusion

References

02_Vinciarelli_1125_EF_Ch02

A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions

2 Key applications of microwave remote sensing

2.1 Land cover classification

2.2 Flood detection and monitoring

2.3 Soil moisture estimation

2.4 Forest monitoring and biomass estimation

2.5 Sea ice classification and monitoring

2.6 Urban mapping and infrastructure monitoring

2.7 Ship detection and maritime surveillance

2.8 Earthquake damage assessment

3 Future scope

4 Conclusion

03_Vinciarelli_1125_EF_Ch03

Fundamentals of active and passive microwave remote sensing: principles and applications

1.1 Overview of remote sensing

1.2 Importance of microwave remote sensing in modern applications

1.3 Active versus passive remote sensing: key distinctions

2 Theoretical foundations of microwave remote sensing

2.1 Electromagnetic spectrum and microwave bands

2.2 Interaction of microwave radiation with the Earth's surface

2.3 Scattering, reflection, and absorption mechanisms

2.4 Polarization in microwave remote sensing

3 Active microwave remote sensing

3.1 Principles of radar systems

3.2 Applications of active microwave remote sensing

4 Passive microwave remote sensing

4.1 Principles of passive systems

4.1.1 Blackbody radiation and brightness temperature

4.1.2 Passive microwave sensors: radiometers.

4.2 Applications of passive microwave remote sensing

4.2.1 Atmospheric monitoring: precipitation, temperature, and humidity profiling

5 Data analysis in microwave remote sensing

5.1 Preprocessing of microwave data

5.1.1 Noise reduction and calibration techniques

5.1.2 Georeferencing and terrain correction

5.2 Feature extraction techniques

6 Challenges, limitations, and future trends in microwave remote sensing

7 Conclusion

04_Vinciarelli_1125_EF_Ch04

Comprehensive overview of active and passive microwave remote sensing satellite sensors

2 Active microwave remote sensing

2.1 Principle of RADAR

2.2 RADAR equation

2.3 Applications of active microwave sensing

3 Passive microwave remote sensing

3.1 Principle of passive microwave remote sensing

3.2 Types of radiometers

3.3 Applications of passive microwave remote sensing

4 Comparative analysis of active and passive microwave remote sensing

5 Image processing in remote sensing

6 Challenges and limitations

7 Future trends and development

8 Conclusion

05_Vinciarelli_1125_EF_Ch05

Essentials of RADAR remote sensing and AI integration

1.1 Overview of RADAR remote sensing

1.2 The role of AI in remote sensing

1.3 Importance of RADAR data in modern AI applications

2 RADAR remote sensing fundamentals

2.1 Basic principles of RADAR

2.2 Types of RADAR systems

2.3 Data acquisition and processing

3 AI in remote sensing

3.1 Overview of ML and DL

3.2 AI techniques for remote sensing data

3.2.1 Algorithms of classification

3.2.2 Change detection

4 Object detection and segmentation

5 Feature extraction

6 Applications in environmental monitoring

6.1 Deforestation and forest monitoring

6.2 Flood mapping and water resource management.

6.3 Soil moisture estimation

6.4 Urban area classification

7 Applications in agriculture and food security

7.1 Precision agriculture

7.2 Crop yield estimation

7.3 Pest and disease detection

7.4 Irrigation management

06_Vinciarelli_1125_EF_Ch06

Fusion of scatterometer and optical remote sensing: enhanced classification and change detection

2 Study site and data

3 Methods

3.1 Preprocessing

3.2 Pan-sharpening

3.3 Classification

3.4 Change detection

3.4.1 Change magnitude

3.4.2 Change direction

3.5 Validation

4 Results and discussion

5 Conclusion

07_Vinciarelli_1125_EF_Ch07

AI-powered urban infrastructure monitoring using RADAR-based remote sensing

1.1 Urban infrastructure monitoring: a growing necessity

1.2 Role of RADAR-based remote sensing in urban monitoring

2 Overview of RADAR technology in urban monitoring

2.1 RADAR fundamentals for urban applications

2.2 RADAR capabilities in urban settings

2.3 RADAR limitations in urban environments

3 AI in urban infrastructure monitoring: applications and practical insights

3.1 AI-driven feature extraction for urban structures

3.2 AI for change detection in urban infrastructure

3.3 AI-powered RADAR-based urban monitoring: key applications

4 Urban growth and expansion monitoring using RADAR and AI

4.1 Detecting urban expansion

4.2 Monitoring changes in land use

4.3 Tracking environmental impacts of urbanization

5 Case studies

5.1 AI-driven RADAR monitoring of a city's road network

5.2 AI-powered RADAR surveillance for urban bridge health monitoring

5.3 Using AI and RADAR for monitoring urban flood defenses

08_Vinciarelli_1125_EF_Ch08.

Fusion of the optical and microwave images for cloud removal

1.1 Remote sensing

1.2 Fusion and its need

1.3 Fusion algorithms for microwave and optical images

2 Dataset and area of interest

3 Methodology: process followed for fusion

3.1 Input raw images

3.2 Preprocessing

3.3 Fusion technique used: NNDiffuse pansharpening

4 Result interpretation and analysis

09_Vinciarelli_1125_EF_Ch09

Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making

2 Fundamentals of RADAR remote sensing

3 AI integration in RADAR remote sensing

4 Literature survey

5 Emerging AI techniques in RADAR applications

6 Case studies and applications

10_Vinciarelli_1125_EF_Ch10

Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology

1.1 Background on RADAR technology

1.2 Emergence of IoT in RADAR systems

2 Fundamentals of RADAR technology

2.2 Despite these valuable applications, RADAR systems have several limitations

2.3 Specific challenges in agriculture monitoring

3 The Importance of satellite imagery in agriculture

3.1 Types of satellite data (multispectral and hyperspectral)

3.2 Case studies of IoT and satellite data integration in agriculture

3.2.1 Crop monitoring

3.2.2 Soil moisture analysis

3.2.3 Yield prediction

3.2.3.1 Key benefits

4 Machine learning and AI in processing RADAR and satellite data

5 Precision agriculture: role of advanced computing

6 Practical applications of RADAR, IoT, and satellite imagery in agriculture

6.1 Use of satellite imagery for monitoring vegetation and crops

6.2 Practical use in agriculture.

6.3 Integration of IoT for precision farming

6.4 Case studies of agricultural projects utilizing RADAR, IoT, and satellite data

6.4.1 Sentinel satellites and IoT for crop monitoring in Argentina

6.4.2 Indore soil moisture monitoring in India using Sentinel-1 and IoT sensors

11_Vinciarelli_1125_EF_Ch11

Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change

1.1 Disaster management and mitigation

1.2 Earthquake monitoring and damage assessment

1.3 Flood detection and response

1.4 Landslide prediction and monitoring

1.5 Wildfire detection and suppression

2 Military and defense applications of RADAR remote sensing with artificial intelligence

2.1 Introduction to RADAR in defense

2.2 Automated target detection and classification

2.3 Surveillance and reconnaissance

2.4 Navigation and guidance systems

2.5 Counter-drone technology

2.6 Challenges and ethical considerations

3 Climate change studies

3.1 Introduction

3.2 Monitoring ice sheets and glaciers

3.3 Sea-level rise detection

3.4 Vegetation and carbon cycle monitoring

3.5 Soil moisture and agricultural impacts

3.6 Challenges and future directions

4 Challenges and future directions

4.1 Technical challenges

4.2 Computational challenges

4.3 Ethical and regulatory challenges

4.4 Future directions

5 Case studies: real-world applications and success stories of RADAR remote sensing with AI

5.1 Case study 1: deforestation monitoring in the amazon

5.2 Case study 2: flood detection and prediction in Southeast Asia

6 Comparative analysis of AI techniques in RADAR applications

7 Lessons learned from implemented solutions

8 Future prospects for AI-powered RADAR applications

9 Conclusion

References.

12_Vinciarelli_1125_EF_Ch12.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

3-11-157297-8

OCLC:

1528022892