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Advances in Aerial Sensing and Imaging

Edited by Sandeep Kumar, Nageswara Rao Moparthi, Abhishek Bhola, Ravinder Kaur, A. Senthil, K.M.V.V. Prasad
Copyright: 2024   |   Status: Published
ISBN: 9781394174690  |  Hardcover  |  
432 pages
Price: $225 USD
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One Line Description
This groundbreaking book is a comprehensive guide to the technology found in the
complex field of aerial sensing and imaging, and the real-world challenges that stem from its growing significance and demand.

Audience
Computer science and artificial intelligence researchers working in the fields of aerial sensing and imaging, as well as professionals working in industries such as agriculture, geology, surveying, urban planning, disaster response, etc; this book provides them with practical guidance and instruction on how to apply aerial sensing and imaging for various purposes and stay up-to-date with the latest developments in the domain.

Description
The advent of unmanned aerial vehicles (UAVs), or drones, along with advancements in sensor technology and image processing techniques, has further enhanced the capabilities and applications of aerial sensing and imaging. These developments have opened up new research, innovation, and exploration avenues.
Aerial sensing and imaging have rapidly evolved over the past few decades and have revolutionized several fields, including land cover and usage prediction, crop and livestock management, road accident monitoring, poverty estimation, defense, agriculture, forest fire detection, UAV security issues, and open parking management. This book provides a comprehensive understanding and knowledge of the underlying technology and its practical applications in different domains.

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Author / Editor Details
Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has been granted six patents and successfully filed another ten. He has published more than 100 research papers in various national and international journals and proceedings of reputed national and international conferences.

Nageswara Rao Moparthi, PhD, is a professor at KL University in the Department of Computer and Engineering. He has 13 years of IT industry exposure with major MNC’s and seven years of teaching research experience. He has published 25 articles in reputed Scopus and SCIE/ESCI international journals.

Abhishek Bhola, PhD, is an associate professor in the Department of Computer Science and Engineering at K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has more than seven years of experience in academics and research. Additionally, he has published several research papers in various international journals and conferences.

Ravinder Kaur, PhD, is an assistant professor in the Department of Computer Science and Engineering (CSE) at the University Institute of Engineering, Chandigarh University, Punjab, India. She has published research papers in various renowned international conferences and SCI-indexed journals.

A. Senthil, PhD, is a professor in the Department of Computer Science and Engineering, KLEF Deemed to be University, A.P., India. He has been in teaching and research for the past 21 years, working as a Professor, Head of Department, and Associate Dean in leading institutions. He has also served as an ABET-USA program evaluator for the Computing Accreditation Commission (CAC) for the last four years.

Contributors:
K.M.V.V. Prasad

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Table of Contents
Preface
1. A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope
Abhishek Bhola, Bikash Debnath and Ankita Tiwari
1.1 Introduction
1.2 Literature Work
1.2.1 Based on Camera Axis
1.2.2 Based on Scale
1.2.3 Based on Sensor
1.3 Challenges of Object Detection and Classification in Aerial Images
1.4 Applications of Aerial Imaging in Various Domains
1.5 Conclusions and Future Scope
1.5.1 Conclusions
1.5.2 Future Scope
References
2. Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition
Monali Gulhane and Sandeep Kumar
2.1 Introduction
2.2 Literature Review
2.3 Materials and Methods
2.3.1 Dataset
2.3.2 Model Implemented
2.4 Discussion
2.5 Result Analysis
2.6 Conclusion
References
3. Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML
Munish Kumar, Poonam Jaglan and Yogesh Kakde
3.1 Introduction
3.2 Related Work
3.3 Number of Accidents, Fatalities, and Injuries: 2016–2022
3.3.1 Accidents Statistics in India
3.3.2 Accidents Statistics in Haryana
3.4 Proposed Methodology
3.4.1 ROI and Line Selection
3.4.2 Motion Detection
3.4.3 Single-Stage Clustering
3.4.4 Feature Fusion Process
3.4.5 Second-Stage Clustering
3.4.6 Tracking Objects
3.4.7 Classification
3.5 Result Analysis
3.6 Conclusion
References
4. A Machine Learning Approach for Poverty Estimation Using Aerial Images
Nandan Banerji, Sreenivasulu Ballem, Siva Mala Munnangi and Sandeep Mittal
4.1 Introduction
4.2 Background and Literature Review
4.3 Proposed Methodology
4.3.1 Data Acquisition
4.3.2 Pre-Processing
4.3.3 Feature Extraction
4.3.4 Data Integration
4.3.5 Model Development
4.3.6 Validation
4.3.7 Visualization and Analysis
4.3.8 Policy and Program Development
4.4 Result and Discussion
4.5 Conclusion and Future Scope
References
5. Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects
Ajay Kumar Singh and Suneet Gupta
5.1 Introduction
5.2 UAVs Classification
5.2.1 Comparison of Various UAVs
5.3 Agricultural Use of UAVs
5.4 UAVs in Livestock Farming
5.5 Challenges
5.6 Conclusion
References
6. An Introduction to Deep Learning-Based Object Recognition and Tracking for Enabling Defense Applications
Nitish Mahajan, Aditi Chauhan and Monika Kajal
6.1 Introduction
6.2 Related Work
6.2.1 Importance of Object Monitoring and Surveillance in Defense
6.2.2 Need for Object Monitoring and Surveillance in Defense
6.2.3 Object Detection Techniques
6.2.4 Object Tracking Techniques
6.3 Experimental Methods
6.3.1 Experimental Setup and Dataset
6.3.2 DataSetVISdrone 2019
6.3.3 Experimental Setup
6.4 Results and Outcomes
6.4.1 Comparison Results
6.4.2 Training Results
6.5 Conclusion
6.6 Future Scope
References
7. A Robust Machine Learning Model for Forest Fire Detection Using Drone Images
Chahil Choudhary, Anurag and Pranjal Shukla
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Result and Discussion
7.5 Conclusion and Future Scope
References
8. Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation
Swathi Gowroju, Shilpa Choudhary, Sandhya Raajaani and Regula Srilakshmi
8.1 Introduction
8.2 Related Work
8.3 Proposed Method
8.3.1 Pixelwise Classification Method
8.3.2 Morphological Processing
8.4 Datasets
8.5 Results and Discussion
8.5.1 Analysis of the Proposed Method
8.6 Conclusion
References
9. Implementation Analysis of Ransomware and Unmanned Aerial Vehicle Attacks: Mitigation Methods and UAV Security Recommendations
Sidhant Sharma, Pradeepta Kumar Sarangi, Bhisham Sharma and Girija Bhusan Subudhi
9.1 Introduction
9.2 Types of Ransomwares
9.3 History of Ransomware
9.4 Notable Ransomware Strains and Their Impact
9.4.1 CryptoLocker (2013)
9.4.2 CryptoWall (2014)
9.4.3 TeslaCrypt (2015)
9.4.4 Locky (2016)
9.4.5 WannaCry (2017)
9.4.6 NotPetya (2017)
9.4.7 Ryuk (2018)
9.4.8 REvil (2019)
9.4.9 Present-Day Ransomware Families
9.5 Mitigation Methods for Ransomware Attacks
9.6 Cybersecurity in UAVs (Unmanned Aerial Vehicles)
9.6.1 Introduction on FANETS
9.6.2 Network Security Concerning FANETs
9.6.3 UAV Security Enhancement
9.6.4 Limitations in UAVs
9.6.5 Future Scope
9.7 Experimental analysis of Wi-Fi Attack on Ryze Tello UAVs
9.7.1 Introduction
9.7.2 Methodology
9.8 Results and Discussion
9.9 Conclusion and Future Scope
References
10. A Framework for Detection of Overall Emotional Score of an Event from the Images Captured by a Drone
P.V.V.S. Srinivas, Dhiren Dommeti, Pragnyaban Mishra and T.K. Rama Krishna Rao
10.1 Introduction
10.1.1 Need for Emotion Recognition
10.1.2 Applications of Drones in Deep Learning
10.2 Literature Review
10.3 Proposed Work
10.3.1 Extraction of Images from a Drone
10.3.2 Proposed CNN Model
10.4 Experimentation and Results
10.4.1 Dataset Description
10.5 Future Work and Conclusion
References
11. Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module
Swathi Gowroju, Shilpa Choudhary, Medipally Rishitha, Singanaboina Tejaswi, Lankala Shashank Reddy and Mallepally Sujith Reddy
11.1 Introduction
11.2 Literature Survey
11.3 Proposed System
11.3.1 Common Forged Feature Network
11.3.2 Features Extraction
11.3.3 Features Classification and Classification Network
11.3.4 Label Prediction
11.3.5 Contrastive Learning
11.3.6 Binary Cross-Entropy Loss
11.4 Results
11.4.1 Experimental Settings
11.4.2 Performance Comparison
11.4.3 LBP Visualized Results
11.4.4 Training Convergence
11.5 Conclusion
References
12. Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning
Harish Padmanaban P. C. and Yogesh Kumar Sharma
12.1 Introduction
12.2 Proposed Framework Optimized Parking Space Identifier (OPSI)
12.2.1 Framework Components
12.2.2 Learning Module: Adaptive Prediction of Parking Space Availability
12.2.3 System Design
12.2.4 Tools and Usage
12.2.5 Architecture
12.2.6 Implementation Techniques and Algorithms
12.2.7 Existing Methods and Workflow Model
12.2.8 Hyperparameter for OPSI
12.3 Potential Impact
12.3.1 Claims for the Accurate Detection of Fatigue
12.3.2 Similar Study and Results Analysis
12.4 Application and Results
12.4.1 Algorithm and Results
12.4.2 Implementation Using Python Modules
12.5 Discussion and Limitations
12.5.1 Discussion
12.5.2 Limitations
12.6 Future Work
12.6.1 Integration with Autonomous Vehicles
12.6.2 Real-Time Data Analysis
12.6.3 Integration with Smart Cities
12.7 Conclusion
References
13. Graphical Password Authentication Using Python for Aerial Devices/Drones
Sushma Singh and Dolly Sharma
13.1 Introduction
13.2 Literature Review
13.3 Methodology
13.4 A Brief Overview of a Drone and Authentication
13.4.1 Password Authentication
13.4.2 Types of Password Authentication Systems
13.4.3 Graphical Password Authentication
13.4.4 Advantages and Disadvantages of Graphical Passwords
13.5 Password Cracking
13.6 Data Analysis
13.7 Discussion
13.8 Conclusion and Future Scope
References
14. A Study Centering on the Data and Processing for Remote Sensing Utilizing from Annoyed Aerial Vehicles
Vandna Bansla, Sandeep Kumar, Vibhoo Sharma, Girish Singh Bisht and Akanksha Srivastav
14.1 Introduction
14.2 An Acquisition Method for 3D Data Utilising Annoyed Aerial Vehicles
14.3 Background and Literature of Review
14.4 Research Gap
14.5 Methodology
14.6 Discussion
14.7 Conclusion
References
15. Satellite Image Classification Using Convolutional Neural Network
Pradeepta Kumar Sarangi, Bhisham Sharma, Lekha Rani and Monica Dutta
15.1 Introduction
15.2 Literature Review
15.3 Objectives of this Research Work
15.3.1 Novelty of the Research Work
15.4 Description of the Dataset
15.5 Theoretical Framework
15.6 Implementation and Results
15.6.1 Data Visualization
15.6.1.1 Class-Wise Data Count
15.6.1.2 Class-Wise Augmented Data Count
15.6.2 Implementation of MobileNetV3
15.6.2.1 Visualization of a Sample of Training Images
15.6.2.2 Visualization of Executed Codes of MobileNetV3
15.6.2.3 Training Results of MobileNetV3
15.6.2.4 Classifications of Errors on Test Sets of MobileNetV3
15.6.2.5 Confusion Matrix of MobileNetV3
15.6.2.6 Classification Report of MobileNetV3
15.6.3 Implementation of EfficientNetB0
15.6.3.1 Visualization of a Sample of Training Images
15.6.3.2 Visualization of Executed Codes of EfficientNetB0
15.6.3.3 Training Results of EfficientNetB0
15.6.3.4 Classifications of Errors on Test Sets of EfficientNetB0
15.6.3.5 Confusion Matrix of EfficientNetB0
15.6.3.6 Classification Report of EfficientNetB0
15.7 Conclusion and Future Scope
References
16. Edge Computing in Aerial Imaging – A Research Perspective
Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Leena Sri R.
16.1 Introduction
16.1.1 Edge Computing and Aerial Imaging
16.2 Research Applications of Aerial Imaging
16.2.1 Vehicle Imaging
16.2.2 Precision Agriculture
16.2.3 Environment Monitoring
16.2.4 Urban Planning and Development
16.2.5 Emergency Response
16.3 Edge Computing and Aerial Imaging
16.3.1 Research Perspective in Aerial Imaging
16.3.2 Edge Architectures
16.4 Comparative Analysis of the Aerial Imaging Algorithms and Architectures
16.5 Discussion
16.6 Conclusion
References
17. Aerial Sensing and Imaging Analysis for Agriculture
Monika Kajal and Aditi Chauhan
17.1 Introduction
17.2 Experimental Methods and Techniques
17.3 Aerial Imaging and Sensing Applications in Agriculture
17.3.1 Assessing Yield and Fertilizer Response
17.3.2 Plant and Crop Farming
17.3.3 Soil and Field Analysis
17.3.4 Weed Mapping and Management
17.3.5 Plantation Crop
17.3.6 Crop and Spot Spraying
17.3.7 Crop Monitoring
17.4 Aerial Imaging and Sensing Applications in Livestock Farming
17.4.1 Livestock Sensor
17.4.2 Animal Health
17.4.3 Monitoring and Identification of Livestock Farming
17.4.4 Geo Fencing and Virtual Perimeters
17.5 Challenges in Aerial Sensing and Imaging in Agriculture and Livestock Farming
17.5.1 Technical Limitations of Aerial Sensing and Imaging in Agriculture and Livestock Farming
17.6 Conclusion
References
Index

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