Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years. This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data.
Part A: Methodology Introduction; Object, data and sensor modelling;
Feature extraction from images and point clouds: Fundamentals, advances and
trends; A short survey on supervised classification in remote; Context-based
classification; Toward a framework for quality assessment in remote sensing
applicationsPart B: Application From raw 3D point clouds to semantic
objects; Traffic extraction and characterization from optical remote sensing
data; Object extraction in image sequences; A process-based model approach to
predict future land-use changes and link biodiversity with soil erosion in
Chile; Interferometric SAR Image analysis for 3D building reconstruction;
Detection and classification of collapsed buildings after a strong earthquake
by means of laser scanning and image analysis; A settlement process analysis
in coastal Benin - confronting scarce data availability in developing
countriesPart C: Conclusion Benchmarking - a basic requirement for effective
performance evaluation; Remote sensing and computer vision image analysis:
summary and recent trends
Professor Stefan Hinz, Director of the Institute of Photogrammetry & Remote SensingDr. Andreas Braun, Institite for Regional ScienceProfessor Martin Weinmann, Institute of Photogrammetry and Remote Sensing, all at Karlsruhe Institute of Technology, Germany
