Jain / Khoshelham / Zhu Proceedings of UASG 2019

Unmanned Aerial System in Geomatics
1. Auflage 2020
ISBN: 978-3-030-37393-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 51, 414 Seiten, eBook

Reihe: Lecture Notes in Civil Engineering

This volume gathers the latest advances, innovations, and applications in the field of geographic information systems and unmanned aerial vehicle (UAV) technologies, as presented by leading researchers and engineers at the 1
st
International Conference on Unmanned Aerial System in Geomatics (UASG), held in Roorkee, India on April 6-7, 2019. It covers highly diverse topics, including photogrammetry and remote sensing, surveying, UAV manufacturing, geospatial data sensing, UAV processing, visualization, and management, UAV applications and regulations, geo-informatics and geomatics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.

Zielgruppe

Research

Autoren/Hrsg.

Jain, Kamal

Khoshelham, Kourosh

Zhu, Xuan

Tiwari, Anuj

Inhaltsverzeichnis

1;Contents;6
2; A Comparative Study of Drone and High Resolution Satellite Data for Detailed Land Use/Land Cover Mapping;10
2.1;1 Introduction;10
2.2;2 Objectives;12
2.3;3 Study Area;12
2.4;4 Methodology;13
2.5;5 Results and Discussion;14
2.6;6 Conclusions;18
2.7;References;18
3; Assessment of Low-Cost Unmanned Aerial Systems for Engineering Surveys;20
3.1;1 Introduction;20
3.1.1;1.1 Unmanned Aerial Systems;21
3.1.2;1.2 Engineering Surveys;21
3.2;2 Study Area;22
3.3;3 Data Acquisition and Analysis;22
3.3.1;3.1 Instrument Used i.e. UAS;22
3.3.2;3.2 Image Acquisition Process;23
3.3.3;3.3 Analysis;23
3.4;4 Conclusion and Applications;25
3.4.1;4.1 Conclusion;25
3.4.2;4.2 Applications in Civil Engineering;26
3.5;References;27
4; Comparing Sensors for Feature Extraction;28
4.1;1 Introduction;29
4.2;2 Materials and Methods;30
4.3;3 Results;31
4.4;4 Discussion;34
4.5;5 Conclusions;34
4.6;References;35
5; Survey in Closed Environments Through UAS Technology. Methodological Approaches to the Study and Image Processing of Religious Furnishings;36
5.1;1 Introduction;36
5.2;2 Related Works;38
5.2.1;2.1 Case Study;38
5.3;3 TLS and UAV Technology: Problems and Resolutions;40
5.3.1;3.1 Data Processing;42
5.4;4 3D Modelling and Formal-Geometric Analysis;44
5.5;5 Conclusions;44
5.6;References;46
6; Integration of Lidar Data in Topographical Feature Extraction from Very High-Resolution Aerial Imagery;48
6.1;1 Introduction;48
6.2;2 Study Area and Data Resources;49
6.3;3 Methodology;49
6.4;4 Results and Discussions;51
6.5;5 Conclusions;52
6.6;References;52
7; Automatic Extraction of Roads from UAV Using Thresholding and Morphometric Parameters;54
7.1;1 Introduction;54
7.2;2 Study Area;55
7.3;3 Methodology;56
7.4;4 Results;56
7.5;5 Conclusions;59
7.6;References;60
8; Detection of Water Body Using Very High-Resolution UAV SAR and Sentinel-2 Images;61
8.1;1 Introduction;62
8.2;2 Study Area and Data Sources;63
8.3;3 Methodology;63
8.4;4 Results and Analysis;66
8.5;5 Conclusion;67
8.6;References;72
9; Comparative Study on Crop Type Classification Using Support Vector Machine on UAV Imagery;74
9.1;1 Introduction;74
9.1.1;1.1 Support Vector Machine;75
9.2;2 Study Area;77
9.3;3 Datasets and Methodology;78
9.3.1;3.1 Datasets;78
9.3.2;3.2 Methodology;78
9.4;4 Results and Discussion;80
9.4.1;4.1 Orthomosaic;80
9.4.2;4.2 Classification and Accuracy assessment;81
9.5;5 Conclusion;83
9.6;References;83
10; Drone-Based Sensing for Leaf Area Index Estimation of Citrus Canopy;85
10.1;1 Introduction;86
10.2;2 Materials and Methods;87
10.2.1;2.1 Site Description, Ground Truth LAI, and Acquisition of Images from UAV;87
10.2.2;2.2 Green Canopy Cover and LAI Estimation from Drone-Based Images;90
10.3;3 Results and Discussion;92
10.3.1;3.1 Comparison of Estimated LAI with Its Ground Truth;92
10.3.2;3.2 Critical Analysis of the Limiting Factors of LAI Estimation;93
10.4;References;94
11; Dynamics of Target Detection Using Drone Based Hyperspectral Imagery;96
11.1;1 Introduction;97
11.2;2 Method and Materials;97
11.2.1;2.1 Target Detector Algorithms;97
11.2.2;2.2 Experimental Design;99
11.3;3 Results and Discussion;100
11.4;4 Conclusion;101
11.5;References;102
12; Blockchain and UAV: Security, Challenges and Research Issues;103
12.1;1 Introduction;103
12.2;2 Scope and Objective;104
12.3;3 Overview of Blockchain and UAANET;105
12.3.1;3.1 Architecture of Block-Chain;105
12.3.2;3.2 UAV Communication Architectures;105
12.3.3;3.3 UAANET as a Subset of MANET;107
12.3.4;3.4 Model Algorithm to Implement Blockchain in UAANET;107
12.4;4 UAANET Security Requirement and Challenges;108
12.4.1;4.1 Vulnerabilities in UAANETs;108
12.4.2;4.2 Existing Attacks in UAANET;108
12.5;5 Research Issues and Current Projects;109
12.5.1;5.1 UAV and Blockchain Together: Projects Around World;110
12.6;6 Conclusion;110
12.7;References;111
13; Placement Optimization of Surveillance Cameras: Visibility Analysis;112
13.1;1 Introduction;112
13.1.1;1.1 UAV;112
13.1.2;1.2 Visibility Analysis;113
13.1.3;1.3 Camera Surveillance;113
13.1.4;1.4 Isovists in Visibility Analysis;113
13.2;2 Study Area and Datasets;114
13.2.1;2.1 Study Area;114
13.3;3 Methodology and Data Preparation;115
13.3.1;3.1 Flight Planning and Image Acquisition;116
13.3.2;3.2 Image Processing;116
13.3.3;3.3 DSM and Orthomosaic Generation;116
13.3.4;3.4 Data Preparation for the Sample Area;116
13.3.5;3.5 Sample Camera Locations;117
13.3.6;3.6 Model Creation and Application;120
13.3.7;3.7 Visibility Map Generation;122
13.4;4 Results;122
13.5;5 Conclusion;122
13.6;References;124
14; Application of Unmanned Aerial Vehicle (UAV) for Damage Assessment of a Cultural Heritage Monument;125
14.1;1 Introduction;126
14.2;2 Study Area and Data Set;127
14.3;3 Methodology;128
14.4;4 Results and Discussions;130
14.5;5 Conclusion;132
14.6;References;132
15; Conceptual Design and Comparative CFD Analyses on Unmanned Amphibious Vehicle for Crack Detection;134
15.1;1 Introduction;135
15.1.1;1.1 Unmanned Amphibious Vehicle (UAV);135
15.1.2;1.2 Objective;136
15.2;2 Design Methodologies Involved in UAV;136
15.2.1;2.1 Introduction;136
15.2.2;2.2 UAV Requirements—Important Parameters for UAV;137
15.2.3;2.3 Design of an Advanced UAV;137
15.2.4;2.4 Composite Material;138
15.3;3 Computational Analysis Results;138
15.3.1;3.1 Hydrodynamic Analysis;139
15.3.2;3.2 Aerodynamic Analysis;139
15.3.3;3.3 Comparative Analysis;142
15.4;4 Health Monitoring Using Unmanned Amphibious Vehicle;145
15.4.1;4.1 Crack Detection on Dam Using Image Processing;145
15.5;5 Conclusion;149
15.6;References;149
16; Conceptual Design and Optimization of Flexible Landing Gear for Tilt-Hexacopter Using CFD;151
16.1;1 Advanced Multi-rotor UAV;151
16.2;2 Conceptual Design Study;152
16.2.1;2.1 Design Tool;152
16.2.2;2.2 Tilt-Hexacopter Without Landing Gear;152
16.2.3;2.3 Tilt-Hexacopter with Model-1 Landing Gear;153
16.2.4;2.4 Tilt-Hexacopter with Model-2 Landing Gear;154
16.3;3 Numerical Simulation;155
16.3.1;3.1 Introduction;155
16.3.2;3.2 Boundary Condition;156
16.3.3;3.3 Result;157
16.3.4;3.4 Comparative Analysis of Forces;167
16.4;4 Conclusion;173
16.5;References;174
17; Review of Inpainting Techniques for UAV Images;175
17.1;1 Introduction;175
17.1.1;1.1 Image Inpainting Problem;176
17.2;2 Image Inpainting Techniques;177
17.2.1;2.1 Diffusion Based Image Inpainting;177
17.2.2;2.2 Texture Based Image Inpainting;178
17.2.3;2.3 Exemplar Based Inpainting;179
17.2.4;2.4 Hybrid Based Inpainting;180
17.2.5;2.5 CNN based inpainting;182
17.3;3 Quality Assessment Measures for Inpainted Image;182
17.3.1;3.1 Structure Based;184
17.3.2;3.2 Saliency Based;185
17.4;4 Conclusion;187
17.5;References;187
18; A Fuzzy Sliding Mode Control Design for Quadcopter;190
18.1;1 Introduction;190
18.2;2 Mathematical Model of Quadcopter;191
18.3;3 Design Procedure of Controller;192
18.4;4 Results and Discussion;194
18.5;5 Conclusions;196
18.6;References;198
19; Unmanned Aerial Vehicles: Vulnerability to Cyber Attacks;200
19.1;1 Introduction;200
19.2;2 Vulnerabilities;201
19.2.1;2.1 Transceiver Level;201
19.2.2;2.2 Control Center Level;202
19.2.3;2.3 Communication Channel Level;202
19.3;3 Types of Attacks;202
19.3.1;3.1 Active Attacks;203
19.3.2;3.2 Passive Attacks;203
19.4;4 Attacks and Their Risk Factors;203
19.4.1;4.1 Man-in-the-Middle Attack;203
19.4.2;4.2 Denial of Service Attack;205
19.4.3;4.3 Command Injection Attack;205
19.4.4;4.4 Privilege Escalation Attack;206
19.4.5;4.5 IP Spoofing Attack;206
19.5;5 Cyber Attacks on UAVs;206
19.6;6 Prevention of Vulnerabilities in UAV;207
19.6.1;6.1 Communication Channel Level;207
19.6.2;6.2 Transceiver Level;207
19.6.3;6.3 Control Center Level;207
19.7;7 Results;208
19.8;8 Conclusion;208
19.9;References;209
20; Perpetual Solar Potential of a Village by Machine Learning and Feature Extraction in UAV;211
20.1;1 Introduction;211
20.1.1;1.1 Study Area;212
20.2;2 Methodology and Discussion;212
20.2.1;2.1 Data Required;212
20.2.2;2.2 Solar Radiation and Potential;213
20.3;3 Estimation;214
20.3.1;3.1 Automatic Feature Extraction Technique;216
20.4;4 Suitable Location of Solar Panels;217
20.5;5 Conclusion;218
20.6;References;221
21; Comparison of Performance of Artificial Neural Network (ANN) and Random Forest (RF) in the Classification of Land Cover Zones of Urban Slum Region;222
21.1;1 Introduction;222
21.2;2 Study Area;224
21.3;3 Methodology;224
21.3.1;3.1 Workflow;224
21.3.2;3.2 Data Collection and Preprocessing;224
21.3.3;3.3 Artificial Neural Network (ANN);225
21.3.4;3.4 Random Forest;228
21.3.5;3.5 Accuracy Assessment;229
21.4;4 Result and Discussion;230
21.5;5 Conclusion;232
21.6;References;232
22; Identification of Urban Slums Using Classification Algorithms—A Geospatial Approach;234
22.1;1 Introduction;235
22.1.1;1.1 Identification of Urban Slums;235
22.1.2;1.2 Analysis of Point Cloud Information;236
22.1.3;1.3 Classification Algorithms—An Overview;236
22.2;2 Study Area and Datasets;237
22.3;3 Methods;238
22.3.1;3.1 Selection of Training Samples;238
22.3.2;3.2 Orthomosaic Dataset Classification;239
22.3.3;3.3 Processing LAS Dataset;242
22.4;4 Results and Discussion;243
22.4.1;4.1 Analysis of Classified Results;243
22.4.2;4.2 Accuracy Assessment;243
22.4.3;4.3 Analysis of Point Cloud Classified Results;243
22.4.4;4.4 Validating Classified Results from Orthomosaic and Point Cloud Datasets;245
22.4.5;4.5 Discussion;247
22.4.6;4.6 Future Works;248
22.5;5 Conclusion;248
22.6;References;248
23; Estimation of Forest Tree Heights and Crown Diameter Using High Resolution Images from UAV: A Case Study of Kalesar, Haryana;250
23.1;1 Introduction;250
23.2;2 Study Area and Dataset;251
23.3;3 Methodology;252
23.3.1;3.1 Creation of Canopy Height Model;254
23.3.2;3.2 Calculation of Tree Heights;254
23.3.3;3.3 Inverse Watershed Segmentation;256
23.4;4 Results and Discussion;256
23.4.1;4.1 Canopy Height Model;256
23.4.2;4.2 Estimated Height of Forest Trees;257
23.4.3;4.3 Estimated Crown Diameter;258
23.5;5 Conclusion;258
23.6;References;259
24; Object Based Automatic Detection of Urban Buildings Using UAV Images;261
24.1;1 Introduction;262
24.2;2 Study Area and Datasets;263
24.3;3 Methodology;263
24.3.1;3.1 Object Based Classification;265
24.3.2;3.2 Multiresolution Segmentation;265
24.3.3;3.3 Rule Based Classification;266
24.4;4 Accuracy Assessment;267
24.5;5 Results and Analysis;268
24.5.1;5.1 Object Based Image Classification;268
24.5.2;5.2 Multiresolution Segmentation;268
24.5.3;5.3 Rule Based Classification;269
24.5.4;5.4 Accuracy Assessment;270
24.6;6 Discussion;271
24.7;7 Conclusion;272
24.8;References;272
25; Micro Level Hydrological Planning and Assessment of Tank Irrigation System;275
25.1;1 Introduction;276
25.2;2 Study Area;276
25.3;3 Methodology;277
25.3.1;3.1 UAV Images;278
25.3.2;3.2 Channel Extraction;278
25.3.3;3.3 Rainfall-Runoff Modelling;279
25.3.4;3.4 Crop Water Requirement;280
25.4;4 Results and Discussion;281
25.4.1;4.1 Channel Extraction;281
25.4.2;4.2 Rainfall-Runoff Modelling;281
25.4.3;4.3 Crop Water Requirement;281
25.5;5 Conclusion;283
25.6;References;284
26; Cost-Effective Real-Time Aerial Surveillance System Using Edge Computing;285
26.1;1 Introduction;285
26.2;2 Related Work;286
26.3;3 Proposed Methodology;287
26.3.1;3.1 System Overview;287
26.3.2;3.2 Edge Computing On-Board Motion Detection;289
26.3.3;3.3 Cloud Based Object Detection;289
26.3.4;3.4 User Interface;290
26.4;4 Experimental Result and Analysis;290
26.4.1;4.1 Experimental Setup;290
26.4.2;4.2 Results and Analysis;292
26.5;5 Conclusion;294
26.6;References;295
27; The Potential of UAV Based Remote Sensing for Monitoring Hindu Kush Himalayan Glaciers;296
27.1;1 Introduction;297
27.2;2 Literature Review;298
27.3;3 Changes in Himalayan Glacier and the Need for UAV Based Studies;299
27.4;4 Applications of UAV for Monitoring Himalayan Glacier;300
27.4.1;4.1 Mass Balance Analysis;300
27.4.2;4.2 Monitoring Debris-Covered Glaciers;301
27.4.3;4.3 GLOF Studies;302
27.4.4;4.4 Temporal Change Analysis;302
27.4.5;4.5 Geomorphological Mapping;303
27.4.6;4.6 Other Applications;303
27.5;5 Benefits and Challenges;304
27.6;6 Conclusion;304
27.7;References;305
28; A Review of UAV Regulations and Policies in India;310
28.1;1 Introduction;310
28.2;2 Background;311
28.3;3 Methodology;311
28.4;4 Database;312
28.5;5 International Context: UAV Regulation Across the World;312
28.6;6 Analysis;312
28.7;7 National Context: UAV Regulation in India;313
28.8;8 Current Regulations (Drone Policy 1.0);313
28.9;9 Proposed Regulations (Drone Policy 2.0);318
28.10;10 Future Trends and Challenges;319
28.11;11 Conclusion;320
28.12;References;320
29; Multi Frequency Polarimetric Decomposition of UAVSAR Data;321
29.1;1 Introduction;322
29.2;2 Study Area;322
29.3;3 Dataset;323
29.4;4 Methodology;324
29.5;5 Results;328
29.6;References;331
30; Analyzing the Effect of Distribution Pattern and Number of GCPs on Overall Accuracy of UAV Photogrammetric Results;332
30.1;1 Introduction;333
30.2;2 Related Work;333
30.3;3 Methodology;334
30.4;4 Workflow;335
30.4.1;4.1 Number of GCPs and their Distribution Pattern;336
30.5;5 Preprocessing;338
30.6;6 Results and Discussions;339
30.6.1;6.1 Quantitative Analysis;339
30.6.2;6.2 Qualitative Analysis;343
30.7;7 Conclusion;346
30.8;References;346
31; CityGML Based 3D Modeling of Urban Area Using UAV Dataset for Estimation of Solar Potential;348
31.1;1 Introduction;348
31.2;2 Study Area and Data Used;350
31.2.1;2.1 Study Area;350
31.2.2;2.2 Data Used;350
31.3;3 Methodology;351
31.4;4 Results and Discussion;352
31.4.1;4.1 City Information Model Geodatabase;352
31.4.2;4.2 Normalized Differential Surface Model (nDSM);352
31.4.3;4.3 Building and Tree Height Estimation;353
31.4.4;4.4 CityGML Based 3D Urban City Model;353
31.4.5;4.5 Shadow Analysis;355
31.4.6;4.6 Solar Potential Estimation;355
31.5;5 Conclusion;360
31.6;References;360
32; Comparative Computational Analysis on High Stable Hexacopter for Long Range Applications;361
32.1;1 Multi-rotor UAV;361
32.1.1;1.1 Studies on Inclined Arm Hexacopter;362
32.2;2 Conceptual Design;362
32.2.1;2.1 Design Stability;362
32.2.2;2.2 Modeling of Conceptual Design in CATIA;362
32.3;3 Numerical Simulation;365
32.3.1;3.1 Initialization of Numerical Simulation;365
32.3.2;3.2 Boundary Conditions;365
32.4;4 Result and Discussion;380
32.4.1;4.1 At 5 m/s;380
32.4.2;4.2 At 10 m/s;381
32.5;5 Conclusion;381
32.6;References;382
33; A Summarization of Collision Avoidance Techniques for Autonomous Navigation of UAV;384
33.1;1 Introduction;384
33.2;2 Related Work;385
33.3;3 Review on Collision Avoidance Methods;386
33.3.1;3.1 Geometry Based Collision Avoidance Methods;387
33.3.2;3.2 Sense and Avoid Collision Avoidance Methods;388
33.3.3;3.3 Optimization-Based Collision Avoidance Methods;388
33.3.4;3.4 Potential Field Collision Avoidance Approaches;389
33.3.5;3.5 Vision Based Collision Avoidance Methods;389
33.4;4 Summary;390
33.5;5 Conclusion;390
33.6;References;390
34; Developing Intelligent Fire Alarm System and Need of UAV;393
34.1;1 Introduction;394
34.2;2 Background;394
34.3;3 Developing Intelligent Fire Alarm System;395
34.4;4 Technical Specification of the Prototype of IFD Is as Follows;398
34.5;5 Overall Algorithm for the UAV-IFAS;398
34.6;6 Results and Discussion;400
34.7;7 Conclusion;402
34.8;References;404
35; Smart Agriculture: The Age of Drones in Agriculture;405
35.1;1 Introduction;406
35.1.1;1.1 LiDAR;406
35.1.2;1.2 Multi-spectral and Hyper-spectral;406
35.1.3;1.3 Thermal;406
35.2;2 Advantages;407
35.3;3 Prior Research;407
35.3.1;3.1 Honeycomb AgDrone System;408
35.3.2;3.2 EBEE SQ-SenseFly;408
35.3.3;3.3 DJI Agras MG-1;409
35.4;4 Concept and Methodology;410
35.5;5 Future Work;412
35.6;6 Summary;412
35.7;References;414