Structure from Motion in the Geosciences

Structure from Motion with Multi View Stereo provides hyperscale landform models using images acquired from standard compact cameras and a network of ground control points. The technique is not limited in temporal frequency and can provide point cloud data comparable in density and accuracy to those generated by terrestrial and airborne laser scanning at a fraction of the cost. It therefore offers exciting opportunities to characterise surface topography in unprecedented detail and, with multi–temporal data, to detect elevation, position and volumetric changes that are symptomatic of earth surface processes. This book firstly places Structure from Motion in the context of other digital surveying methods and details the Structure from Motion workflow including available software packages and assessments of uncertainty and accuracy. It then critically reviews current usage of Structure from Motion in the geosciences, provides a synthesis of recent validation studies and looks to the future by highlighting opportunities arising from developments in allied disciplines. This book will appeal to academics, students and industry professionals because it balances technical knowledge of the Structure from Motion workflow with practical guidelines for image acquisition, image processing and data quality assessment and includes case studies that have been contributed by experts from around the world. INDICE: Abbreviations .About the companion website .Chapter 1: Introduction to Structure from Motion for the geosciences .1.1. The geosciences and related disciplines .1.2. Aim and scope of this book .1.3. The time and the place .1.4. What is Structure from Motion? .1.5. Structure of this book .References .Chapter 2: The place of Structure from Motion: a new paradigm in topographic surveying? .2.1. Introduction .2.2. Direct topographic surveying .2.3. Remote digital surveying .2.4. Chapter summary .References .Further Reading/Resources .Chapter 3: Background to Structure from Motion .3.1. Introduction .3.2. Feature Detection .3.3. Keypoint Correspondence .3.4. Identifying Geometrically Consistent Matches .3.5. Structure from Motion .3.6. Scale and Georeferencing .3.7. Optimization of Image Alignment .3.8. Clustering for Multi View Stereo .3.9. Multi View Stereo Image Matching Algorithms .3.10. Summary .References .Further Reading/Resources .Chapter 4: Structure from Motion in practice .4.1. Introduction .4.2. Platforms .4.3. Sensors .4.4. Acquiring images and control data .4.5. Software .4.6. Point cloud viewers .4.7. Filtering .4.8. Generating digital elevation models (DEMs) from point clouds .4.9. Key issues .4.10. Chapter summary .References .Further reading .Chapter 5: Quality assessment: quantifying error in Structure from Motion–derived topographic data .5.1. Introduction .5.2. Validation Data Sets .5.3. Validation Methods .5.4. Survey Platform .5.5. Error Metrics .5.6. Distribution of Ground Control Points .5.7. Terrain .5.8. Software .5.9. Camera .5.10. Summary .References .Further Reading/Resources .Chapter 6: Current applications of Structure from Motion in the geosciences .6.1. Introduction .6.2. Use of SfM–MVS–derived orthophotograph mosaics .6.3. Use of SfM–MVS for 3D point clouds .6.4. Use of SfM–MVS for gridded topography .6.5. Combined orthophotograph and point cloud analysis .6.6. Crossing temporal scales: Examples of change detection to suggest process dynamics .6.7. Practitioner–based SfM–MVS .6.8. Chapter summary .References .Further Reading/Resources .Chapter 7: Developing Structure from Motion for the geosciences: future directions .7.1. Introduction .7.2. Developments in hardware .7.3. Progressive automation of acquisition .7.4. Efficient management and manipulation of photographs .7.5. Point cloud generation and decimation .7.6. Real–time SfM–MVS and instant maps: Simultaneous Localization And Mapping (SLAM) .7.7. Augmented reality .7.8. Detection of object or surface motion: non–rigid SfM (NRSfM) .7.9. Chapter summary .References .Further Reading/Resources .Chapter 8: Concluding recommendations .8.1. Key Recommendation 1: get under the bonnet of SfM–MVS to become more critical end–users .8.2. Key Recommendation 2: get coordinated to understand the sources and magnitudes of error .8.3. Key Recommendation 3: focus on the research question .8.4. Key Recommendation 4: focus your efforts on data processing .8.5. Key Recommendation 5: learn from other disciplines .8.6. Key Recommendation 6: harness the democratising power of SfM–MVS .Index

• ISBN: 978-1-118-89584-9
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 208
• Fecha Publicación: 26/08/2016
• Nº Volúmenes: 1
• Idioma: Inglés