
Advances in Aerial Sensing and Imaging
By: Sandeep Kumar (Editor), Nageswara Rao Moparthi (Editor), Abhishek Bhola (Editor), Ravinder Kaur (Editor), A. Senthil (Editor)
Hardcover | 6 March 2024 | Edition Number 1
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In the digital world, computer vision is a trending technology and it is highly in demand in the security and surveillance industry, as well as for self-driven cars and entertainment applications. This surge in the popularity of computer vision is due to the emergence of state-of-the-art deep learning technologies that can solve computer vision tasks with very high accuracy, which was considered unachievable a decade ago. Computer vision is used to provide human intelligence and understanding to computers. Digital images are used as input to the computer and later, machines can identify the objects in an image. Vehicle detection and counting at the border of countries and states/cities have become famous through aerial images because of security concerns. Sixty-seven percent of the world's population will be cities by 2030, so ground infrastructure cannot keep up and is costly to overhaul. Hence, vehicle detection and classification are important research areas in vision and computing applications. Due to the alarming improvement in areal imaging and vehicle tracking on highways, detection is always challenging due to vehicles' different shapes and sizes and different resolutions of satellite images.
The motivation for this book is twofold: object recognition has unique characteristics and the demands of the object recognition module are increasing rapidly in the market through aerial images. Object detection provides an intersection between computer vision and deep learning methods and it gains the attention of many researchers because of real-world applications. Aerial images are beneficial for providing timely rescue operations during natural disasters such as floods, fires, etc. The use of aerial imagery to provides immediate emergency help to humans. With the rapidly growing importance of military and security applications, object surveillance has become a critical area of research. Aerial imagery is used to predict poverty estimates in several places worldwide. According to the report of the Compound Annual Growth Rate (CAGR), the aerial data services market is going to increase up to $23.4 billion from 2017 to 2025.
The objective of this book is to provide more relevant information about aerial recognition system-based challenges, applications, and security. Emerging AI-based technology will play a vital role in reducing crime. This book will assist readers in understanding how AI is widely employed for further research and professionals for providing solutions to different societal problems. Readers will learn how AI offers numerous applications and has been beneficial for different industries. Artificial intelligence will have significant ramifications for humanity, to the point where it will redefine what it means to be human. AI technology aims to develop intelligent solutions for different industrial sectors and societies and offers to complete tasks in a stipulated period with more efficiency and accuracy. This revolutionary technology helps individuals automate their tasks by making traditional systems smarter.
Preface xv
1 A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope 1
Abhishek Bhola, Bikash Debnath and Ankita Tiwari
1.1 Introduction 2
1.2 Literature Work 5
1.3 Challenges of Object Detection and Classification in Aerial Images 17
1.4 Applications of Aerial Imaging in Various Domains 21
1.5 Conclusions and Future Scope 26
2 Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition 33
Monali Gulhane and Sandeep Kumar
2.1 Introduction 34
2.2 Literature Review 35
2.3 Materials and Methods 37
2.4 Discussion 40
2.5 Result Analysis 41
2.6 Conclusion 43
3 Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML 47
Munish Kumar, Poonam Jaglan and Yogesh Kakde
3.1 Introduction 48
3.2 Related Work 49
3.3 Number of Accidents, Fatalities, and Injuries: 2016–2022 52
3.4 Proposed Methodology 53
3.5 Result Analysis 61
3.6 Conclusion 63
4 A Machine Learning Approach for Poverty Estimation Using Aerial Images 69
Nandan Banerji, Sreenivasulu Ballem, Siva Mala Munnangi and Sandeep Mittal
4.1 Introduction 70
4.2 Background and Literature Review 73
4.3 Proposed Methodology 76
4.4 Result and Discussion 82
4.5 Conclusion and Future Scope 83
5 Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects 87
Ajay Kumar Singh and Suneet Gupta
5.1 Introduction 88
5.2 UAVs Classification 90
5.3 Agricultural Use of UAVs 96
5.4 UAVs in Livestock Farming 101
5.5 Challenges 104
5.6 Conclusion 105
6 An Introduction to Deep Learning-Based Object Recognition and Tracking for Enabling Defense Applications 109
Nitish Mahajan, Aditi Chauhan and Monika Kajal
6.1 Introduction 110
6.2 Related Work 111
6.3 Experimental Methods 121
6.4 Results and Outcomes 122
6.5 Conclusion 123
6.6 Future Scope 125
7 A Robust Machine Learning Model for Forest Fire Detection Using Drone Images 129
Chahil Choudhary, Anurag and Pranjal Shukla
7.1 Introduction 130
7.2 Literature Review 131
7.3 Proposed Methodology 133
7.4 Result and Discussion 135
7.5 Conclusion and Future Scope 142
8 Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation 145
Swathi Gowroju, Shilpa Choudhary, Sandhya Raajaani and Regula Srilakshmi
8.1 Introduction 146
8.2 Related Work 147
8.3 Proposed Method 149
8.4 Datasets 153
8.5 Results and Discussion 154
8.6 Conclusion 161
9 Implementation Analysis of Ransomware and Unmanned Aerial Vehicle Attacks: Mitigation Methods and UAV Security Recommendations 165
Sidhant Sharma, Pradeepta Kumar Sarangi, Bhisham Sharma and Girija Bhusan Subudhi
9.1 Introduction 166
9.2 Types of Ransomwares 167
9.3 History of Ransomware 168
9.4 Notable Ransomware Strains and Their Impact 171
9.5 Mitigation Methods for Ransomware Attacks 184
9.6 Cybersecurity in UAVs (Unmanned Aerial Vehicles) 185
9.7 Experimental analysis of Wi-Fi Attack on Ryze Tello UAVs 194
9.8 Results and Discussion 198
9.9 Conclusion and Future Scope 206
10 A Framework for Detection of Overall Emotional Score of an Event from the Images Captured by a Drone 213
P.V.V.S. Srinivas, Dhiren Dommeti, Pragnyaban Mishra and T.K. Rama Krishna Rao
10.1 Introduction 214
10.2 Literature Review 216
10.3 Proposed Work 220
10.4 Experimentation and Results 223
10.5 Future Work and Conclusion 230
11 Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module 245
Swathi Gowroju, Shilpa Choudhary, Medipally Rishitha, Singanaboina Tejaswi, Lankala Shashank Reddy and Mallepally Sujith Reddy
11.1 Introduction 246
11.2 Literature Survey 247
11.3 Proposed System 250
11.4 Results 256
11.5 Conclusion 264
12 Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning 267
Harish Padmanaban P. C. and Yogesh Kumar Sharma
12.1 Introduction 267
12.2 Proposed Framework Optimized Parking Space Identifier (OPSI) 270
12.3 Potential Impact 281
12.4 Application and Results 284
12.5 Discussion and Limitations 289
12.6 Future Work 290
12.7 Conclusion 290
13 Graphical Password Authentication Using Python for Aerial Devices/Drones 295
Sushma Singh and Dolly Sharma
13.1 Introduction 296
13.2 Literature Review 297
13.3 Methodology 298
13.4 A Brief Overview of a Drone and Authentication 299
13.5 Password Cracking 305
13.6 Data Analysis 307
13.7 Discussion 309
13.8 Conclusion and Future Scope 309
14 A Study Centering on the Data and Processing for Remote Sensing Utilizing from Annoyed Aerial Vehicles 313
Vandna Bansla, Sandeep Kumar, Vibhoo Sharma, Girish Singh Bisht and Akanksha Srivastav
14.1 Introduction 314
14.2 An Acquisition Method for 3D Data Utilising Annoyed Aerial Vehicles 315
14.3 Background and Literature of Review 317
14.4 Research Gap 319
14.5 Methodology 319
14.6 Discussion 321
14.7 Conclusion 327
15 Satellite Image Classification Using Convolutional Neural Network 333
Pradeepta Kumar Sarangi, Bhisham Sharma, Lekha Rani and Monica Dutta
15.1 Introduction 334
15.2 Literature Review 335
15.3 Objectives of this Research Work 336
15.4 Description of the Dataset 337
15.5 Theoretical Framework 337
15.6 Implementation and Results 339
15.7 Conclusion and Future Scope 350
16 Edge Computing in Aerial Imaging – A Research Perspective 355
Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Leena Sri R.
16.1 Introduction 355
16.2 Research Applications of Aerial Imaging 357
16.3 Edge Computing and Aerial Imaging 366
16.4 Comparative Analysis of the Aerial Imaging Algorithms and Architectures 376
16.5 Discussion 379
16.6 Conclusion 380
17 Aerial Sensing and Imaging Analysis for Agriculture 383
Monika Kajal and Aditi Chauhan
17.1 Introduction 384
17.2 Experimental Methods and Techniques 388
17.3 Aerial Imaging and Sensing Applications in Agriculture 390
17.4 Aerial Imaging and Sensing Applications in Livestock Farming 398
17.5 Challenges in Aerial Sensing and Imaging in Agriculture and Livestock Farming 404
17.6 Conclusion 406
References 406
Index 411
ISBN: 9781394174690
ISBN-10: 1394174691
Published: 6th March 2024
Format: Hardcover
Language: English
Number of Pages: 432
Audience: Professional and Scholarly
Publisher: John Wiley & Sons Inc (US)
Country of Publication: GB
Edition Number: 1
Weight (kg): 0.89
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