Advances in Aerial Sensing and Imaging

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope
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
Chapter 2 Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition
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
Chapter 3 Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML
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
Chapter 4 A Machine Learning Approach for Poverty Estimation Using Aerial Images
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
Chapter 5 Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects
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
Chapter 6 An Introduction to Deep Learning-Based Object Recognition and Tracking for Enabling Defense Applications
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
Chapter 7 A Robust Machine Learning Model for Forest Fire Detection Using Drone Images
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Result and Discussion
7.5 Conclusion and Future Scope
References
Chapter 8 Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation
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
Chapter 9 Implementation Analysis of Ransomware and Unmanned Aerial Vehicle Attacks: Mitigation Methods and UAV Security Recommendations
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
Chapter 10 A Framework for Detection of Overall Emotional Score of an Event from the Images Captured by a Drone
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
Chapter 11 Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module
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
Chapter 12 Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning
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
Chapter 13 Graphical Password Authentication Using Python for Aerial Devices/Drones
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
Chapter 14 A Study Centering on the Data and Processing for Remote Sensing Utilizing from Annoyed Aerial Vehicles
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
Chapter 15 Satellite Image Classification Using Convolutional Neural Network
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
Chapter 16 Edge Computing in Aerial Imaging – A Research Perspective
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
Chapter 17 Aerial Sensing and Imaging Analysis for Agriculture
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