Advances in Aerial Sensing and Imaging E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 592

Veröffentlichungsjahr: 2024

Beschreibung

Advances in Aerial Sensing and Imaging

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.

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.

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.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope

1.1 Introduction

1.2 Literature Work

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

References

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

2.4 Discussion

2.5 Result Analysis

2.6 Conclusion

References

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

3.4 Proposed Methodology

3.5 Result Analysis

3.6 Conclusion

References

4 A Machine Learning Approach for Poverty Estimation Using Aerial Images

4.1 Introduction

4.2 Background and Literature Review

4.3 Proposed Methodology

4.4 Result and Discussion

4.5 Conclusion and Future Scope

References

5 Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects

5.1 Introduction

5.2 UAVs Classification

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

6.1 Introduction

6.2 Related Work

6.3 Experimental Methods

6.4 Results and Outcomes

6.5 Conclusion

6.6 Future Scope

References

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

8 Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation

8.1 Introduction

8.2 Related Work

8.3 Proposed Method

8.4 Datasets

8.5 Results and Discussion

8.6 Conclusion

References

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

9.5 Mitigation Methods for Ransomware Attacks

9.6 Cybersecurity in UAVs (Unmanned Aerial Vehicles)

9.7 Experimental Analysis of Wi-Fi Attack on Ryze Tello UAVs

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

10.1 Introduction

10.2 Literature Review

10.3 Proposed Work

10.4 Experimentation and Results

10.5 Future Work and Conclusion

References

11 Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module

11.1 Introduction

11.2 Literature Survey

11.3 Proposed System

11.4 Results

11.5 Conclusion

References

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)

12.3 Potential Impact

12.4 Application and Results

12.5 Discussion and Limitations

12.6 Future Work

12.7 Conclusion

References

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

13.5 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

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

15.1 Introduction

15.2 Literature Review

15.3 Objectives of this Research Work

15.4 Description of the Dataset

15.5 Theoretical Framework

15.6 Implementation and Results

15.7 Conclusion and Future Scope

References

16 Edge Computing in Aerial Imaging – A Research Perspective

16.1 Introduction

16.2 Research Applications of Aerial Imaging

16.3 Edge Computing and Aerial Imaging

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

17.1 Introduction

17.2 Experimental Methods and Techniques

17.3 Aerial Imaging and Sensing Applications in Agriculture

17.4 Aerial Imaging and Sensing Applications in Livestock Farming

17.5 Challenges in Aerial Sensing and Imaging in Agriculture and Livestock Farming

17.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Layer expansion of the proposed VGG16 model.

Table 2.2 Qualified analysis of various studies for LULC categorization in com...

Chapter 3

Table 3.1 Total number of accidents, fatalities, and persons injured from 2016...

Table 3.2 Year-wise details of accidents, fatalities, and persons injured in H...

Table 3.3 Year-wise details of help extended to the accident victims in Haryan...

Table 3.4 Detection rate of accident comparison with other methods.

Chapter 4

Table 4.1 Comparison of our proposed methods with existing methods.

Chapter 5

Table 5.1 Applications of UAVs in various industries.

Table 5.2 UAVs comparison by features [17].

Table 5.3 Comparison of the service levels of the various precision agricultur...

Table 5.4 Farming applications of UAVs.

Chapter 7

Table 7.1 Traditional machine learning model.

Table 7.2 Robust machine learning model.

Chapter 8

Table 8.1 Parameters used in model construction.

Table 8.2 Comparison of model accuracy on independent model evaluation.

Chapter 9

Table 9.1 Notable ransomware strains (2020–2021).

Table 9.2 Comparative analysis of famous ransomware strains.

Chapter 10

Table 10.1 Emotional score of individual faces for the image (Example 1) captu...

Table 10.2 Emotional score of individual faces for the image captured from dro...

Table 10.3 Emotional score of individual faces for the image captured from dro...

Chapter 11

Table 11.1 Literature survey of forgery image detection from the past decade.

Table 11.2 Trainable parameters of the model.

Table 11.3 Loss values at each epoch.

Table 11.4 Performance analysis using various pre-trained models.

Chapter 12

Table 12.1 Existing models for finding optimal parking space

.

Table 12.2 Epochs and layers.

Table 12.3 Hyperparameters and values.

Table 12.4 Result analysis of existing models.

Table 12.5 Confusion matrix.

Table 12.6 Result analysis of existing vs. proposed models.

Chapter 13

Table 13.1 Comparison between password authentication systems.

Chapter 14

Table 14.1 Literature of review.

Chapter 16

Table 16.1 Algorithm comparison.

Table 16.2 Architecture comparison.

Chapter 17

Table 17.1 Advantages and disadvantages of aerial sensing and imaging in agric...

Table 17.2 Advantages and disadvantages of aerial sensing and imaging in lives...

Table 17.3 Comparison of the values/data for the NDVI and the EVI.

Table 17.4 List of some of the Zoonoses that can be identifies using UAVs [28]...

List of Illustrations

Chapter 1

Figure 1.1 Aerial imaging growth market (2023–2032).

Figure 1.2 Overall structure of aerial image types.

Figure 1.3 Types of aerial images based on camera axis.

Figure 1.4 Types of aerial images based on scale.

Figure 1.5 Types of aerial images based on sensor.

Figure 1.6 Sample image of atmosphere effects on aerial images.

Figure 1.7 Effects on aerial images due to camera quality.

Figure 1.8 Sample image of object overlapping.

Figure 1.9 Sample image of time-consuming real-time.

Figure 1.10 Sample image of aspect ratios.

Figure 1.11 Small views of the object in aerial image.

Figure 1.12 Difficulty in labeling the data.

Figure 1.13 Sample image of drone direction.

Figure 1.14 Various applications of aerial imaging techniques in different dom...

Chapter 2

Figure 2.1 Proposed model system with improved VGG16.

Figure 2.2 Sample images from EuroSAT data set.

Figure 2.3 Architecture diagram of VGG16.

Figure 2.4 Loss and accuracy graph.

Figure 2.5 Evaluation of model.

Figure 2.6 Output with label images.

Figure 2.7 Comparative analysis of accuracy on proposed model with existing mo...

Chapter 3

Figure 3.1 Accidents, fatalities, and persons injured from 2016 to 2021.

Figure 3.2 Details of accidents, death, and injured from 2016 to 2020 (June).

Figure 3.3 Details of help extended to the accident victims in Haryana.

Figure 3.4 Module A for classification by utilizing SVM.

Figure 3.5 Module B for sending the alert to the rescue team.

Figure 3.6 Comparison of conflict detection rate graph with other techniques a...

Chapter 4

Figure 4.1 Aggregate district development in 2001 (as per census), 2011 (as pe...

Figure 4.2 Flow chart of overall work.

Figure 4.3 Proposed methods compared to previous studies.

Chapter 5

Figure 5.1 Classification of UAVs.

Figure 5.2 Fixed-wing UAV.

Figure 5.3 Single rotor UAV.

Figure 5.4 Multi-rotor UAVs (a) Tricoptor, (b) Quadcopter, (c) Hexacopter, and...

Figure 5.5 Design of hybrid VTOL UAV.

Figure 5.6 UAV flight time versus weight [17].

Figure 5.7 UAV capturing raw images for soil analysis.

Figure 5.8 UAV-based seed planting.

Figure 5.9 Spraying operation by UAV.

Figure 5.10 Cattle herd’s heat map [36].

Figure 5.11 Employing relay UAVs to identify animals.

Figure 5.12 Aerial mustering.

Figure 5.13 Commercial drones for agriculture. (a) agricultural spraying drone...

Chapter 6

Figure 6.1 Various outcomes of an efficient monitoring and surveillance infras...

Figure 6.2 Box proposal generation and final box outputs of yolo-based object ...

Figure 6.3 Steps involved in object tracking using Kalman filter here Oi is th...

Figure 6.4 Showing particle filter-based tracking flowchart.

Figure 6.5 Correlation filter algorithm for object tracking [29].

Figure 6.6 The architecture of efficient layer aggregation networks [30].

Figure 6.7 Scaling concatenation-based models [30].

Figure 6.8 Snapshots from the Visdrone 2019 dataset.

Figure 6.9 Graph showing various training parameters for YOLOv7 trained on vis...

Chapter 7

Figure 7.1 Flow chart of proposed work.

Figure 7.2 Proposed drone image.

Figure 7.3 Confusion matrix.

Figure 7.4 Correlation coefficient of proposed work.

Figure 7.5 MAE of proposed work.

Figure 7.6 RMSE of proposed work.

Figure 7.7 Accuracy of proposed work.

Chapter 8

Figure 8.1 Proposed UNet model for semantic segmentation.

Figure 8.2 Content window specifying pixel and its network marked in green dot...

Figure 8.3 Sample input images from dataset.

Figure 8.4 Input image and its mask for the preprocessing.

Figure 8.5 Validation loss and IoU plot of the model.

Figure 8.6 Segmentation performed on various sample images.

Figure 8.7 Map of classes with buildings with high vegetation: Labeling error ...

Chapter 9

Figure 9.1 Types of ransomwares.

Figure 9.2 AIDS DOS Trojan horse payload [5].

Figure 9.3 Cryptolocker (2013) [14].

Figure 9.4 TeslaCrypt shutdown [24].

Figure 9.5 WannaCry attack flow [31].

Figure 9.6 WanaDecrypt0r ransomware note [31].

Figure 9.7 NotPetya ransom note [36].

Figure 9.8 Ryuk ransom note [39].

Figure 9.9 Ryuk ransomware attack flow [40].

Figure 9.10 TOR payment page from victim’s computer due to REvil [47].

Figure 9.11 Wallpaper changed after REvil infection [48].

Figure 9.12 Instruction text file for REvil ransom payment [48].

Figure 9.13 Percentage of security threats in FANETS [53].

Figure 9.14 Jamming attack in FANET.

Figure 9.15 Eavesdropping in FANET.

Figure 9.16 Man-in-the-Middle attack in FANET.

Figure 9.17 Impersonation attack.

Figure 9.18 Replay attack in FANET network.

Figure 9.19 DoS attack in a FANET network.

Figure 9.20 Tello network being shown in the scan [54].

Figure 9.21 connected clients with the target [54].

Figure 9.22 DeAuth process [54].

Figure 9.23 Wireshark analysis [54].

Figure 9.24 Hashed key [54].

Figure 9.25 Password found [54].

Figure 9.26 2021 Sector data affected by ransomware [55].

Figure 9.27 Notable ransomware families 2020–2021 [56].

Figure 9.28 Ransomware damage prediction from 2015–2021 [57].

Chapter 10

Figure 10.1 Flowchart of emotion recognition.

Figure 10.2 Proposed model architecture.

Figure 10.3 Accuracy and loss plot obtained from FER 2013.

Figure 10.4 Values obtained from the proposed model through FER 2013.

Figure 10.5 Heat map through FER 2013.

Figure 10.6 (a) Image captured by drone. (b) Faces detected from image. (c) Ex...

Figure 10.7 (a) Image captured by drone. (b) Faces detected from image. (c) Ex...

Figure 10.8 Accuracy and loss plot obtained from CREMAD.

Figure 10.9 Classification matrix obtained from the proposed model through CRE...

Figure 10.10 Heatmap of the FERCNN model through CREMAD.

Figure 10.11 (a) Image captured by drone. (b) Face detected from drone image. ...

Chapter 11

Figure 11.1 Proposed forged face detector (image source [18]).

Figure 11.2 Forged feature network.

Figure 11.3 Classification of image.

Figure 11.4 Input pre-processed face images [18].

Figure 11.5 Plot of training and validation classes.

Figure 11.6 Validation accuracy of the proposed system in the training phase.

Figure 11.7 Validation and accuracy plots of the model.

Figure 11.8 The visualized LBP feature maps of the RFID dataset [18].

Figure 11.9 Images after detecting whether the image is forged using own datas...

Figure 11.10 (a) The validation accuracy curves produced by PGGAN (b) The curv...

Chapter 12

Figure 12.1 Depicts the proposed architecture of the OPSI framework.

Figure 12.2 Architecture of YOLO.

Figure 12.3 Workflow of OPSI model for parking availability.

Figure 12.4 Precision recall curve.

Figure 12.5 YOLO3 algorithm to load test images.

Figure 12.6 Resizing the sample image.

Figure 12.7 Non-maximum suppression.

Figure 12.8 Evaluate the model.

Chapter 13

Figure 13.1 A drone in operational state.

Figure 13.2 An Unmanned Aerial Vehicle (UAV).

Figure 13.3 Biometric data (fingerprint, retina scan).

Figure 13.4 Image has been given to the user.

Figure 13.5 Password chosen by the user.

Figure 13.6 Signature scheme.

Figure 13.7 Various images given to the user to choose from.

Figure 13.8 Privacy concerns in drones (This Graph has been adapted from – Big...

Chapter 14

Figure 14.1 Development of forest department. (Source: Statista, 2023).

Figure 14.2 Forest management services. (Source: Statista, 2023).

Figure 14.3 Block diagram of drone detection. (Source: [5]).

Figure 14.4 Distribution of the reviewed studies per (a) Annoyed aerial vehicl...

Figure 14.5 Classification of the annoyed aerial systems [1].

Chapter 15

Figure 15.1 General architecture of a CNN model.

Figure 15.2 Class-wise data count.

Figure 15.3 Augmented data.

Figure 15.4 Sample training images.

Figure 15.5 Execution of MobileNetV3.

Figure 15.6 Training results of accuracy and loss (MobileNetV3).

Figure 15.7 Classification of errors on test sets (MobileNetV3).

Figure 15.8 Confusion matrix (MobileNetV3).

Figure 15.9 Confusion matrix (MobileNetV3).

Figure 15.10 Confusion matrix (EfficientNetB0).

Figure 15.11 Execution of EfficientNetB0.

Figure 15.12 Training results of accuracy and loss of EfficientNetB0.

Figure 15.13 Classification of errors on test sets (EfficientNetB0).

Figure 15.14 Confusion matrix (EfficientNetB0).

Figure 15.15 Classification report (EfficientNetB0).

Chapter 16

Figure 16.1 Applications of aerial imaging.

Figure 16.2 Vehicle imaging.

Figure 16.3 Precision agriculture.

Figure 16.4 Environment monitoring.

Figure 16.5 Emergency response.

Figure 16.6 Edge computing in aerial imaging.

Figure 16.7 Cloudlet-based architecture [27].

Figure 16.8 MEC architecture [28].

Figure 16.9 Distributed edge architecture [29].

Figure 16.10 Fog architecture [30].

Figure 16.11 Federated learning architecture [31].

Chapter 17

Figure 17.1 Flow of energy and information in aerial sensing and imaging.

Figure 17.2 Feature comparison of different types of UAVs.

Figure 17.3 The proposed CM-GM framework [8].

Figure 17.4 The NDVI scale and interpretation (Range varies from -1.0 to 1.0).

Figure 17.5 Various animal surveillance factors addressed by Aerial [34].

Figure 17.6 Geofencing system depicted graphically to track people entering an...

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

Pages

ii

iii

iv

xv

xvi

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Advances in Aerial Sensing and Imaging

Edited by

Sandeep Kumar

Nageswara Rao Moparthi

Abhishek Bhola

Ravinder Kaur

A. Senthil

and

K.M.V.V. Prasad

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-17469-0

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

This 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. 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. Readers will also be able to learn about the latest research, theories, and real-world challenges in this field.

Advances in Aerial Sensing and Imaging is a comprehensive collection of research articles that deeply understand the latest advancements in aerial sensing and imaging. This book serves as a reference guide for researchers, professionals, and students who are interested in exploring the potential of the subject. The other objective of this book is to combine in collaborative the work of multiple authors sharing the same interest in aerial sensing and imaging. The contributors to this book are experts in their respective fields and have provided valuable insights into the latest research and advancements. The chapters herein cover a wide range of topics in diversified research fields.

We thank all the authors who helped us tremendously with their contributions, time, critical thoughts, and suggestions to assemble this peer-reviewed volume, and without whose dedication and hard work this book would not be possible. We also extend our thanks to the reviewers who have provided valuable feedback and helped to improve the quality of the material. The editors are also thankful to Scrivener Publishing and their team members for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during this work.

We hope this book will be valuable for researchers, professionals, and students interested in aerial sensing and imaging. This book will inspire further research and innovation and contribute to developing new applications and technologies. We look forward to new advancements and hope this book will play a small role in shaping the future of this exciting field.

Sandeep Kumar

Nageswara Rao Moparthi

Abhishek Bhola

Ravinder Kaur

A. Senthil

K.M.V.V. Prasad

1A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope

Abhishek Bhola1*, Bikash Debnath2 and Ankita Tiwari3

1Chaudhary Charan Singh Haryana Agricultural University College of Agriculture, Bawal, Haryana, Rewari, India

2Department of Information Technology, Amity University, Kolkata, West Bengal, India

3Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vijayawada, India

Abstract

Aerial images captured by drones or aircraft provide a unique perspective and valuable data in various fields, including agriculture, urban planning, construction, and environmental research. They offer high-resolution images that can be used to create detailed maps, monitor changes over time, and provide clear information not visible from ground level. Despite their many benefits, there are also challenges associated with aerial imaging. These challenges include the cost and availability of equipment, weather conditions and terrain, data management and analysis, privacy concerns, and regulatory issues. Overcoming these challenges requires specialized skills and expertise and careful consideration of ethical and environmental concerns. However, as technology advances, aerial images’ benefits are expanding, enabling new applications, and more detailed analysis. For example, infrared imaging allows for monitoring plant health and identifying areas of water stress, which is particularly useful in agriculture and environmental research. In addition, the ability to cover large areas quickly and efficiently provides a comprehensive view that is impossible with ground-based surveys, making it valuable for infrastructure inspection and urban planning. Despite the challenges, the scope for aerial imaging is expanding rapidly, with advancements in technology enabling new applications and more detailed analysis. As the technology continues to evolve, the benefits of aerial images will only continue to grow, making it an increasingly valuable tool for decision-making and problem-solving in various industries.

Keywords: Aerial images, scale, sensor, camera, machine learning

1.1 Introduction

Aerial sensing and imaging have revolutionized how we observe, measure, and understand the world. This technology captures images, videos, and other data from an elevated platform like an aircraft or drone to understand a particular area or object better [1–3]. With technological advancements, aerial sensing and imaging have become more accessible and cost-effective, making them essential tools for various industries, including agriculture, forestry, urban planning, environmental monitoring, and infrastructure management [4–6]. Aerial sensing and imaging are also used to provide real-time data and insights in emergency response situations, such as natural disasters and search and rescue missions. This paper will explore the various applications and benefits of aerial sensing and imaging and discuss the latest technological advancements [7–9].

The history of aerial sensing and imaging dates back to the 19th century, when French photographer and balloonist Gaspar Félix Tournachon, known as Nadar, captured the first aerial photograph over Paris in 1858. The picture was taken from a hot air balloon at an altitude of 80 meters and provided a bird’s eye view of the city [10–12]. In the early 20th century, advancements in aircraft technology led to the development of aerial photography, which became an essential tool for military surveillance during World War-I [13–16]. During the 1920s and 1930s, aerial photography was used for mapping, surveying, and topographic studies, as well as for scientific research and exploration. In the 1950s and 1960s, aerial sensing and imaging technology evolved with the development of airborne remote sensing systems, which used cameras and sensors mounted on aircraft to capture images and data [17–19]. This technology was used for various applications, including mapping, agriculture, forestry, and geological exploration. In the 1970s and 1980s, satellite remote sensing technology emerged as a new and powerful tool for remote sensing and imaging. Satellites offered a global view of the Earth and provided data on various environmental factors, including climate, vegetation, and oceanography [20–24]. In the 1990s and 2000s, advancements in unmanned aerial vehicle (UAV) technology led to the development of aerial sensing and imaging systems that were more cost-effective, flexible, and accessible than traditional airborne or satellite-based systems. UAVs were used for various applications, including agriculture, environmental monitoring, search and rescue, and infrastructure inspection [25–28]. The global market for unmanned aerial vehicle (UAV) drones is projected to reach 102.38 billion US dollars by 2032, expanding at a compound annual growth rate (CAGR) of 18.2% from 2018 to 2032 as shown in Figure 1.1 [29–31].

Today, aerial sensing and imaging technology continue to evolve, with new and innovative applications and advancements in sensor technology, data analysis, and artificial intelligence. Aerial sensing and imaging technology is being used to monitor and mitigate climate change’s impact, support disaster response and recovery efforts, and improve the efficiency and sustainability of various industries [32–35]. Aerial sensing and imaging technology has a rich and diverse history, spanning over a century of technological advancements and innovation. Today, it continues to be a critical tool for understanding and managing our planet and is poised to play an increasingly important role in the future. Aerial sensing and imaging is a rapidly growing field involving advanced technologies to capture images and data from the air [36–38]. This field has numerous applications in environmental monitoring, disaster response, agriculture, and urban planning. Using unmanned aerial vehicles (UAVs) has revolutionized aerial sensing and imaging, enabling researchers and professionals to collect highresolution data quickly and accurately [39, 40]. If you are considering studying aerial sensing and imaging, there are many compelling reasons to do so. First and foremost, this field is at the forefront of technological innovation, and studying it will expose you to the latest advances in remote sensing and data collection. This is an exciting time to be involved in aerial sensing and imaging, as new technologies such as LiDAR, hyperspectral imaging, and drones are rapidly advancing the field. In addition to technological innovation, there is a growing need for professionals with aerial sensing and imaging expertise [41–43]. As the demand for more accurate and detailed data grows in areas such as environmental monitoring and agriculture, there is a need for skilled professionals who can use aerial sensing and imaging technologies to collect and analyze this data [44, 45]. This presents an excellent opportunity for those with a background in this field to pursue rewarding careers in agriculture, forestry, and environmental science. Studying aerial sensing and imaging also opens up opportunities for research and development. This field has many unanswered questions and options for exploration, and those with the skills and knowledge to tackle these challenges can significantly contribute to the field. Research in this area can lead to new insights into environmental processes, improved mapping, and monitoring techniques, and data analysis and interpretation advancements [46–48]. Finally, studying aerial sensing and imaging can be personally rewarding. It provides an opportunity to work on cutting-edge technology and make a meaningful impact in environmental conservation and disaster response fields. Suppose you are passionate about using technology to solve real-world problems and positively impact society. In that case, studying aerial sensing and imaging may be the perfect fit for you [49, 50].

Figure 1.1 Aerial imaging growth market (2023–2032).

The study of aerial sensing and imaging involves using advanced technologies to capture images and data from the air [51–53]. The following are some of the critical objectives of studying aerial sensing and imaging:

To gain knowledge and understanding of the underlying principles and concepts of remote sensing and imaging techniques. Aerial sensing and imaging rely on various technologies, including LiDAR, radar, multispectral and hyperspectral imaging, and drones [

54

–

56

]. Studying these technologies will enable you to understand how they work, their limitations, and how they can be applied to various applications.

To develop technical skills in using remote sensing and imaging equipment and software. Aerial sensing and imaging involve complex technologies and software for data capture, processing, and analysis. Studying this field will enable you to develop technical skills using remote sensing and imaging equipment, including drones, cameras, and other sensors [

57

–

59

]. You will also learn to process and analyze data using specialized software such as Geographic Information Systems (GIS).

To understand the applications and limitations of aerial sensing and imaging in various fields. Aerial sensing and imaging have many applications in agriculture, forestry, environmental science, and urban planning. Studying aerial sensing and imaging will enable you to understand the potential and limitations of these technologies in various applications [

60

–

62

].

To develop the ability to design and implement aerial sensing and imaging projects. Aerial sensing and imaging projects involve many steps, including project design, data acquisition, processing, analysis, and reporting [

63

–

65

]. Studying aerial sensing and imaging will enable you to develop the skills to design and implement such projects effectively.

To contribute to the advancement of the field through research and development. Aerial sensing and imaging are rapidly evolving fields; much must be explored and discovered. Studying aerial sensing and imaging will enable you to contribute to advancing the area through research and development [

66

,

67

].

Literature study of the aerial imaging and sensing is discussed in Section 1.2 to acquire essential information, insights, and issues. In Section 1.3, the diverse aerial image issues are explored and listed. In Section 1.4, various aerial image applications are discussed. Finally in Section 1.5, the study is concluded by discussing prospective areas for future investigation.

1.2 Literature Work

Aerial images are photographs taken from a high altitude using cameras and sensors. Literature on aerial images covers remote sensing, cartography, urban planning, and disaster response [68–71]. Research focuses on image processing techniques, sensor selection, and applications in various fields, as shown in Figure 1.2.

Figure 1.2 Overall structure of aerial image types.

1.2.1 Based on Camera Axis

Aerial images based on the camera axis refer to photographs taken from a high altitude using a camera pointed straight down as shown in Figure 1.3. This technique is commonly used in cartography, remote sensing, and surveillance applications. Literature on this topic includes studies on image processing techniques to improve the quality and accuracy of aerial images, such as filtering, feature extraction, and classification algorithms. Other research focuses on developing uncrewed aerial vehicles (UAVs) with cameras for efficient data collection. Additionally, studies have explored the use of aerial images for monitoring and mapping environmental changes, urban planning, and disaster response.

Figure 1.3 Types of aerial images based on camera axis.

Vertical Photograph

The use of vertical photographs in aerial images has been studied for many years. This brief literature review will explore some of the research done in this field. One study published in the Journal of Geographical Sciences by author Wei Huang et al. [1], examined the use of vertical photographs in digital photogrammetry. They found that vertical photos have higher accuracy and precision than oblique photographs, making them more suitable for mapping and surveying purposes. Another study published in the International Journal of Remote Sensing by authors John E. Peacock et al. [2], examined the use of vertical photographs in urban remote sensing. They found that vertical photos can provide more accurate and detailed information about the built environment than oblique photographs. This information can be used for urban planning and management purposes. A study published in the Journal of Photogrammetry and Remote Sensing by authors Yves-Louis Desnos et al. [3], examined the use of vertical photographs in terrain mapping. They found that vertical pictures provide a better terrain view, making them more suitable for terrain mapping applications. Another study published in the International Journal of Remote Sensing by authors Xiaojun Cheng et al. [4], examined the use of vertical photographs in land cover classification. They found vertical photos can provide more accurate and reliable information about land cover types than oblique photographs. Finally, a study published in the International Journal of Remote Sensing by authors Nicholas M. Short et al. [5], examined the use of vertical photos in forestry. They found that vertical photographs can provide more accurate information about forest canopy structures, which can be used for forest inventory and management.

Low Oblique Photographs

Low oblique photographs in aerial images have been a topic of interest for many researchers in remote sensing and photogrammetry. The following literature study summarizes some notable works on this topic: “Low Oblique Photography for Survey and Mapping” by E. L. Rayner et al. [6], this early work explores the use of low oblique photography for surveying and mapping purposes. The authors highlight the advantages of low oblique photography over traditional vertical aerial photography, such as the ability to see the sides of buildings and terrain features more clearly. “Low-Oblique Photogrammetry: Application to Geology” by C. W. Wright et al. [7], this study demonstrates the use of low oblique photography in geological mapping. The authors show that low oblique photographs can provide detailed information on the shape and orientation of rock outcrops, which can be helpful in geological mapping and mineral exploration. “Low-Oblique Aerial Photography in Landscape Ecology” by R. T. T. Forman et al. [8], this study uses low oblique photography in landscape ecology. The authors demonstrate how low oblique photographs can provide information on landscape structure and pattern, which can be used to study the effects of land use changes on ecosystems. “Low-Altitude Photogrammetry” by J. R. Jensen [9], this book chapter provides an overview of low-altitude photogrammetry, which includes low oblique photography. The author discusses the advantages and disadvantages of low oblique photography and provides examples of its applications in various fields, such as forestry, agriculture, and urban planning. “Low-Oblique Photogrammetry for Building Damage Assessment” by S. H. Lee et al. [10] this recent study explores the use of low oblique photography for building damage assessment. The authors show that low oblique photographs can provide more detailed information on building damage than vertical aerial photographs, which can be helpful for post-disaster reviews.

High Oblique Photographs

High oblique photographs in aerial images have also been a topic of interest for researchers in remote sensing and photogrammetry. A literature study summarizes some notable works on this topic: “The interpretation of high oblique photographs” by J. B. Campbell [11], this early work focuses on the interpretation of high oblique photographs. The author discusses the advantages and disadvantages of highoblique photographs compared to low-oblique and vertical aerial photographs. The author also provides examples of high oblique photographs used for urban planning and military applications. “High-Oblique Aerial Photography in Forestry” by G. L. Hosford et al. [12], this study explores high oblique photography in forestry. The authors show that highly oblique photographs can provide information on forest structure and composition, which can be helpful for forest inventory and management. “High-Oblique Photography in Archaeology” by R. W. Ehrich [13] this study demonstrates the use of high oblique photography in archaeology. The author shows that highly oblique photographs can provide information on the location and extent of archaeological sites, which can be helpful for site discovery and mapping. “High-Oblique Aerial Photography for Mapping and Environmental Studies” by D. A. Norton et al. [14], explores high oblique photography for mapping and environmental studies. The authors discuss the advantages of high oblique photographs, such as capturing a larger area than vertical aerial photographs and provide examples of their use in various applications, such as wetlands mapping and land use planning. “High-Oblique Aerial Photography for Disaster Assessment and Response” by K. N. Sukumar et al. [15] this recent study demonstrates the use of highoblique photography for disaster assessment and response. The authors show that highly oblique photographs can provide information on the extent and severity of damage caused by natural disasters, which can be helpful in emergency response and planning.

1.2.2 Based on Scale

Aerial images based on scale refer to photographs taken from a high altitude scaled to a particular size for analysis and measurement, as shown in Figure 1.4. Literature on this topic includes studies on aerial images for topographic mapping, urban planning, and resource management. Research has also focused on developing image processing techniques to improve the accuracy of scaled aerial photos, such as orthorectification and geometric correction. Additionally, studies have explored using unmanned aerial vehicles (UAVs) for capturing high-resolution images at a smaller scale for more detailed analysis. The importance of scaling in aerial photos is also discussed concerning the accuracy of measurements, interpretation, and comparison with other data sources.

Figure 1.4 Types of aerial images based on scale.

High Oblique Photographs

Large-scale photographs in aerial images are used for detailed mapping and analysis of small areas. Here is a literature study summarizing some notable works on this topic: “The Use of Large Scale Aerial Photography for Mapping the Geology of Small Areas” by H. E. Gregory et al. [16], this early work focuses on the use of large scale aerial photography for mapping the geology of small areas. The authors discuss the advantages of large-scale photographs, such as capturing detailed landscape features and providing examples of their use in geologic mapping. “Applications of Large-Scale Aerial Photography in Highway Engineering” by W. L. Beadles et al. [17], this study explores large-scale aerial photography in highway engineering. The authors show that large-scale photographs can provide information on terrain features and land use, which can help plan highway routes and design structures. “The Use of Large-Scale Aerial Photography in Forestry” by D. E. Bedford [18], this study demonstrates large-scale aerial photography in forestry. The author shows that large-scale photographs can provide information on forest structure and composition, which can be helpful for forest inventory and management. “Large-Scale Aerial Photography for Environmental Monitoring and Management” by L. M. Joppa et al. [19], this study explores large-scale aerial photography for environmental monitoring and management. The authors discuss the advantages of large-scale photographs, such as capturing detailed information on ecological features and providing examples of their use in various applications, such as land cover classification and habitat monitoring. “Large-Scale Aerial Photography for Precision Agriculture” by J. P. White et al. [20], this recent study demonstrates the use of large-scale aerial photography for precision agriculture. The authors show that large-scale photographs can provide information on crop health and yield variability, which can be helpful for crop management and optimization.

Medium Oblique Photographs

Medium-scale photographs in aerial images are commonly used for general mapping, land-use analysis, and environmental studies. A literature study summarizes some notable works on this topic: “Mapping Urban Land Use Patterns by Medium Scale Aerial Photography” by R. E. Brinkman [21] this early work focuses on using medium-scale aerial photography for mapping urban land-use patterns. The author discusses the advantages of medium-scale photographs, such as the ability to capture enough detail for urban planning and the cost-effectiveness of their production. “Applications of Medium-Scale Aerial Photography in Environmental Studies” by D. W. Johnson et al. [22], explores medium-scale aerial photography in environmental studies. The authors show that medium-scale photographs can provide information on land-use patterns and ecological features, which can be helpful for environmental monitoring and management. “Medium Scale Aerial Photography for Geologic Mapping” by J. L. Smoot [23], this study demonstrates medium-scale aerial photography for geologic mapping. The author shows that medium-scale photographs can provide information on topographic and geologic features, which can be helpful for geologic mapping and mineral exploration. “Medium-Scale Aerial Photography for Coastal Zone Management” by S. S. Raghavan et al. [24], this study explores medium-scale aerial photography for coastal zone management. The authors discuss the advantages of medium-scale photographs, such as capturing information on coastal features and land-use patterns, and provide examples of their use in various coastal zone management applications. “Medium-Scale Aerial Photography for Agricultural Land Use Mapping” by A. M. Al-Rawahy et al. [25], this recent study demonstrates medium-scale aerial photography for agricultural land-use mapping. The authors show that medium-scale photographs can provide information on crop distribution and land-use patterns, which can be helpful in agricultural land-use planning and management.

Small Oblique Photographs

Small-scale photographs in aerial images are commonly used for regional and global mapping, weather forecasting, and climate studies. A literature study summarizing some notable works on this topic: “Small-Scale Aerial Photography in Regional Mapping” by A. R. Gillespie et al. [26], this early work focuses on small-scale aerial photography in regional mapping. The authors discuss the advantages of small-scale photographs, such as the ability to capture a large area at once and the low cost of their production. “Small Scale Aerial Photography for Weather Forecasting” by E. J. Zipser et al. [27], explores small-scale aerial photography for weather forecasting. The authors show that small-scale photographs can provide information on cloud patterns and atmospheric conditions, which can help forecast weather patterns. “Small Scale Aerial Photography for Climate Studies” by J. R. Christy et al. [28], this study demonstrates small-scale aerial photography for climate studies. The authors show that small-scale photographs can provide information on regional temperature patterns and climate change, which can help predict future climate trends. “Small-Scale Aerial Photography for Global Mapping” by T. W. Foresman [29], this study explores small-scale aerial photography for global mapping. The author discusses the advantages of small-scale photographs, such as the ability to capture a large area and the consistency of their production over time. “Small Scale Aerial Photography for Land Cover Classification” by S. K. Srivastava et al. [30], this recent study demonstrates small-scale aerial photography for land cover classification. The authors show that small-scale photographs can provide information on land-use patterns and vegetation cover, which can be helpful for ecological studies and land-use planning.

1.2.3 Based on Sensor

Aerial images based on sensors refer to photographs taken from a high altitude using different sensors, such as cameras, Lidar, and thermal sensors as shown in Figure 1.5. Literature on this topic includes studies on using these sensors for various applications, including topographic mapping, land cover classification, vegetation analysis, and disaster response. Research has also focused on developing image processing techniques to improve the accuracy and quality of data captured by different types of sensors. Additionally, studies have explored combining multiple sensors to capture more comprehensive data for various applications. The importance of selecting the appropriate sensor for a particular application is also discussed concerning cost, accuracy, and availability.

Figure 1.5 Types of aerial images based on sensor.

Black and White Panchromatic

Black and white panchromatic aerial images have been essential for various applications such as mapping, land use classification, change detection, and urban planning. Several recent studies have focused on analyzing and enhancing black-and-white panchromatic aerial images. One of the essential studies in this field was conducted by Cheng et al. [31], in this study, the authors proposed a method for enhancing black-and-white panchromatic aerial images using a Laplacian pyramid-based approach. The proposed method uses a multi-scale Laplacian pyramid to decompose the input image into different scales. The contrast and brightness of each scale are then enhanced using other methods, and the improved rankings are combined to obtain the final enhanced image. The proposed method was evaluated on a black-and-white panchromatic aerial image dataset, and the results showed significant improvement in image quality. Another critical study was conducted by Asikainen et al. [32], in this study, the authors evaluated the performance of different image fusion methods for black-and-white panchromatic aerial images. The authors compared the performance of three other image fusion methods: Brovey transform, principal component analysis (PCA), and intensity-hue-saturation (IHS) transform. The authors evaluated the interpretation of these methods using different quality metrics, such as mean square error (MSE) and peak signal-to-noise ratio (PSNR). The results showed that the Brovey and IHS transform performed better than the PCA methods. In another study, Liu et al. [33] proposed a method for enhancing black-and-white panchromatic aerial images using a deep convolutional neural network (CNN). The proposed method uses a CNN to learn the mapping between the input and enhanced images. The authors used a black-and-white panchromatic aerial image dataset to train and test the CNN. The results showed significant improvement in image quality, and the proposed method outperformed the traditional methods.

Natural Color Imagery

Natural color imagery in aerial images is captured by remote sensing systems replicating the natural colors visible to the human eye. These images have been extensively used in land cover classification, vegetation mapping, and urban planning applications. Several studies have been conducted to analyze and enhance the quality of natural color imagery in aerial images. Zhang et al. [34] led critical research in this field. In this study, the authors proposed a method for enhancing natural color imagery in aerial images using a local mean-based approach. The proposed method uses a local mean filter to improve the contrast and brightness of the input image. The filter size is adjusted based on the regional image characteristics, and the enhanced image is obtained by combining the filtered image with the original image. The proposed method was evaluated on a dataset of natural color aerial images, and the results showed significant improvement in image quality. Another critical study was conducted by Chen et al. [35]. In this study, the authors proposed a method for correcting natural color aerial images using a histogram-matching approach. The proposed method uses a histogram-matching algorithm to adjust the input image’s color distribution to match the reference image’s color distribution. The reference image is selected based on its similarity to the input image in terms of spatial and spectral characteristics. The proposed method was evaluated on a dataset of natural color aerial images, and the results showed significant improvement in image quality. In another study, Chen et al. [36] proposed a method for enhancing natural color aerial images using a convolutional neural network (CNN). The proposed method uses a CNN to learn the mapping between the input and enhanced photos. The authors used a dataset of natural color aerial images to train and test the CNN. The results showed significant improvement in image quality, and the proposed method outperformed the traditional methods.

Infrared or Thermal Imagery

Infrared or thermal imagery in aerial images is essential for various applications, including crop monitoring, building energy analysis, and search and rescue operations. Infrared or thermal imagery captures the radiation emitted by objects in the form of heat, which can be used to detect temperature differences and other thermal properties. Several studies have been conducted to analyze and enhance the quality of infrared or thermal imagery in aerial images. Jia et al. [37] conducted critical research in this field. In this study, the authors proposed a method for enhancing infrared imagery in aerial images using a deep-learning approach. The proposed method uses a convolutional neural network (CNN) to learn the mapping between the input and enhanced photos. The authors used a dataset of infrared aerial images to train and test the CNN. The results showed significant improvement in image quality, and the proposed method outperformed the traditional methods. Another critical study was conducted by Lin et al. [38]. In this study, the authors proposed a method for detecting building energy loss using thermal imagery in aerial images. The proposed method uses a deep learning approach to classify thermal photos into different categories based on the temperature distribution of the buildings. The authors used a dataset of thermal aerial images to train and test the deep learning model. The results showed that the proposed method could effectively detect building energy loss using thermal imagery in aerial photos. In another study, Stathopoulou et al. [39] proposed a plan for detecting crop stress using thermal imagery in aerial images. The proposed method uses a spectral index approach to identify the thermal anomalies in the crop field. The authors used a dataset of thermal aerial photos to evaluate the proposed method. The results showed that the proposed method could effectively detect crop stress using thermal imagery in aerial images.

Radar Imagery

Radar imagery in aerial images has been widely used for various applications such as land cover mapping, disaster management, and military operations. Radar sensors emit microwaves that penetrate the atmosphere and interact with the Earth’s surface, providing information on the surface properties such as roughness and moisture content. Several studies have been conducted to analyze and enhance the quality of radar imagery in aerial images. Chen et al. [40] conducted critical research in this field. In this study, the authors proposed a method for classifying land cover using polarimetric radar imagery in aerial images. The proposed method uses a machine learning approach based on the support vector machine (SVM) to organize the radar imagery into different land cover classes. The authors used a dataset of polarimetric radar aerial images to train and test the SVM model. The results showed that the proposed method could effectively classify the land cover using polarimetric radar imagery in aerial photos. Another critical study was conducted by Hu et al. [41]. In this study, the authors proposed a method for detecting building damage using radar imagery in aerial images. The proposed method uses a deep learning approach based on the convolutional neural network (CNN) to identify the building damage from the radar imagery. The authors used a dataset of aerial radar images to train and test the CNN model. The results showed that the proposed method could effectively detect building damage using radar imagery in aerial photos. In another study, Liu et al. [42] proposed a method for detecting landslides using radar imagery in aerial images. The proposed method uses a machine learning approach based on the random forest (RF) algorithm to classify the radar imagery into different classes based on the landslide characteristics. The authors used a dataset of aerial radar images to evaluate the proposed method. The results showed that the proposed method could effectively detect landslides using radar imagery in aerial photos.

1.3 Challenges of Object Detection and Classification in Aerial Images

Despite their many benefits, aerial images also present several challenges. One of the biggest challenges is the cost and availability of equipment, such as drones or aircraft, cameras, and processing software. The quality and clarity of the images can also be impacted by the atmosphere and terrain. Furthermore, the immense quantities of data generated by aerial photography can be challenging to manage and analyze, necessitating specialized techniques, and software. Privacy concerns and regulatory issues, such as restrictions on flying over certain areas or obtaining permits, can also limit the use of aerial images. Finally, there may be ethical considerations around the potential impact of aerial photos on wildlife, cultural heritage sites, and other sensitive areas.

Atmospheric Conditions

: Due to scattering, the existence of nanoparticles (smoke or dust), components of gases in the atmosphere, clouds, and the rainfall season contribute to diminishing contrast, so the ideal moment for pictures is when the weather is clear, as shown in

Figure 1.6

.

Camera/Film/Filter Combination

: To assure excellent image quality, digital cameras without deformation is used. Depending on the situation, various lens/focal length/film/filter configurations can be used, as shown in

Figure 1.7

.

Figure 1.6 Sample image of atmosphere effects on aerial images.

Figure 1.7 Effects on aerial images due to camera quality.

Figure 1.8 Sample image of object overlapping.

Object Overlap

: One of the disadvantages of dividing photographs is that the same substance may appear in multiple images. This results in double identification and tracking errors, as depicted in

Figure 1.8

. Elements near one another during detection may also have overlapping boundary frames. One solution to this issue is to upsample via a scrolling window, searching for tiny, densely packed objects.

Speed for Real-Time Detection

: Object identification techniques must be exceedingly rapid in estimation time to achieve the real-time needs of video analysis and other applications, as shown in

Figure 1.9

. However, in Aerial images, it is challenging to predict real-time processing to identify the objects.

Figure 1.9 Sample image of time-consuming real-time.

Scaling and Aspect Ratios

: Scale is the ratio between two depictions on a satellite image and the exact location between the same two points/objects on the ground. Due to differences in flying height, the proportions of photos may vary. Additionally, the dimension may differ due to inclination and elevation displacements, as shown in

Figure 1.10

.

Flat and Small View of Objects

:

Figure 1.11

demonstrates that for obliquely captured UAV imagery, the targets of concern are comparatively small and have fewer features, appearing mostly flattened and rectangular. For instance, a UAV imagery of a building only depicts the roof, whereas an earthly image of the same building will include doors, windows, and walls.

Difficulty in Labeling Data

: As illustrated in

Figure 1.12

, even if we could collect many photos, we still need to categorize them. This tedious operation requires delicacy and accuracy, as “garbage in, garbage out” applies. There is no unique algorithm for classifying them.

Figure 1.10 Sample image of aspect ratios.

Figure 1.11 Small views of the object in aerial image.

Figure 1.12 Difficulty in labeling the data.

Figure 1.13 Sample image of drone direction.

Drone Direction

:

Figure 1.13

shows aerial imaging is performed in segments to encompass the designated area from various angles. It is recommended to limit the number of features. Therefore, the stripe drone orientation is maintained along the extent of the area.

1.4 Applications of Aerial Imaging in Various Domains

Aerial images captured by drones or aircraft have various applications across different industries. In agriculture, they can be used to monitor crop health, map fields, and estimate yields. In urban planning, aerial images can aid in creating 3D models, assessing land use, and identifying areas