Download or read book Object based Segmentation and Machine Learning Classification for Landslide Detection from Multi temporal WorldView 2 Imagery written by Owen Patrick Parker and published by . This book was released on 2013 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Object Based Image Analysis written by Thomas Blaschke and published by Springer Science & Business Media. This book was released on 2008-08-09 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Download or read book Image data driven Deep Learning for Slope Stability Analysis written by and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Landslides cause major infrastructural issues, damage the environment, and cause socio-economic disruptions. Therefore, various slope stability analysis methods have been developed to evaluate the stability of slopes and the probability of their failure. This dissertation attempts to take advantage of the recent advancements in remote sensing and computer technology to implement a deep-learning-based landslide prediction method. Considering the novelty of this approach, this dissertation leads with proof-of-concept studies to evaluate and establish the suitability of deep learning models for slope stability analysis. To achieve this, a simulated 2D dataset of slope images was created with different geometries and soil properties. Next, multiclass classification and regression models in deep learning were used to test the performance of the models. The model performance was evaluated in terms of accuracy and computation time, and satisfactory results were obtained. The results indicated the high potential of deep learning methods in slope stability analysis. After confirming the feasibility and suitability of deep learning methods for slope stability analysis, a dataset of real-world image data was needed to test whether the deep learning models can predict landslides for more down-to-earth applications. For this purpose, multi-temporal high-resolution DEM data was used to compile a dataset of landslides that have occurred in a study area. The proposed landslide detection method could detect small to medium-size landslides, which was validated with landslide inventories and Google Earth imagery. The detected landslides were used to train an instance segmentation model, i.e., Mask R-CNN, and a regression model in deep learning. The instance segmentation model was unsuccessful in localizing the landslides in the pre-event images but provided insight into the shortcomings of the adopted procedure and model. The regression model, however, showed encouraging performance in terms of accuracy and computation time.

Download or read book Artificial Intelligence and Machine Learning in Satellite Data Processing and Services written by Sumit Kumar and published by Springer Nature. This book was released on 2023-01-02 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, Artificial Intelligence and Machine Learning in Satellite: Data Processing and Services, presents the selected proceedings of the International Conference on Small Satellites (ICSS 2022) that aims to provide an opportunity for academicians, scientists, researchers, and industry experts, engaged in teaching, research, and development on satellite data processing and its services by employing advanced artificial intelligence-based machine learning techniques. This book covers the application of artificial intelligence and machine learning techniques in various domains of earth observations like natural resources and environmental management, water resources, urban and rural development, climate change, and other contemporary subjects. The book will surely be a valuable asset for beginners, researchers, and professionals working in satellite data processing and services using artificial intelligence and machine learning approaches.

Download or read book Uncertainty in Remote Sensing and GIS written by Giles M. Foody and published by John Wiley & Sons. This book was released on 2003-07-11 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.

Download or read book Multi temporal Landslide Inventories Creation from Visual Interpretation of Satellite Imagery written by Michiel Voermans and published by . This book was released on 2019 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Frontiers of Remote Sensing Information Processing written by C. H. Chen and published by World Scientific. This book was released on 2003 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leaders in the field of remote sensing information processing, this book covers the frontiers of remote sensors, especially with effective algorithms for signal/image processing and pattern recognition with remote sensing data. Sensor and data fusion issues, SAR images, hyperspectral images, and related special topics are also examined. Techniques making use of neural networks, wavelet transforms, and knowledge-based systems are emphasized. A special set of three chapters is devoted to seismic analysis and discrimination. In summary, the book provides an authoritative treatment of major topics in remote sensing information processing and defines new frontiers for these areas. Contents: Data Mining; SAR Image Processing; Wavelet Analysis and Applications; Military Applications of Remote Sensing; Microwave Remote Sensing; Statistical Pattern Recognition; Automatic Target Segmentation; Neural Networks; Change Detection; Seismic Signal Processing; Time Series Prediction; Image Compression; Emerging Topics. Readership: Engineers and scientists dealing with remote sensing data in particular, and signals and images in general; computer scientists involved in software development on geophysical data analysis.

Download or read book Analysis of Landslide Kinematics Using Multi temporal UAV Imagery La Honda California written by Jordan Alexander Carey and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High-resolution topographic data are vital to studies of earth-surface processes. The combination of unmanned aerial vehicle (UAV) photography and structure-from-motion (SfM) digital photogrammetry provide a quickly deployable and cost-effective method for monitoring geomorphic change and landscape evolution. We acquired imagery of an active landslide in La Honda, California using a GPS-enabled quadcopter UAV with a 12.4 megapixel camera. Deep-seated landslides were previously documented in this region during the winter of 1997-98, with movement recurring and the landslide expanding during the winters of 2004-05 and 2005-06. This study documents the kinematics of a new and separate landslide immediately adjacent to the previous ones, throughout the winter of 2016-17. The Scenic Drive landslide is roughly triangular-shaped, deep-seated failure covering an area of approximately 10,000 m2. The area is underlain by SW-dipping late Miocene to Pliocene sandstones and mudstones. A ~3 m-high head scarp stretches along the northeast portion of the slide along a distance of ~100 m. The direction of movement is towards the southwest, with two prominent NW-SE striking extensional grabens and numerous tension cracks across the landslide body. In this thesis, I calculate displaced landslide volumes, derived from changes in elevation, and surface displacements from multi-temporal UAV surveys. Photogrammetric reconstruction of UAV/SfM-derived point clouds allowed creation of seven digital elevation models (DEMs) with spatial resolutions ranging from ~3 to 10 cm per pixel. I derived displacement magnitude, direction and rate by comparing multiple generations of DEMs and orthophotos and estimated displaced volumes by differencing subsequent DEMs creating DEMs of difference (DoDs). I then correlated displacements with total rainfall and rainfall intensity measurements. Geomorphic mapping of the study area identifies major landslide features, such as the head scarp, normal and thrust scarps, extensional grabens, tension cracks, and associated earthflows, documenting dominant surface processes on the slide. Additionally, I compare the accuracy of the UAV/SfM-derived DEM with a DEM sourced from a synchronous terrestrial lidar survey. Conservative measurements yield 5.4 m of maximum horizontal displacement across the central portion of the slide during the monitoring period. Over the course of the monitoring period, ~3,000 m3 of material was displaced by the landslide. Comparisons between the lidar and SfM DEMs showed that the two are comparable in the horizontal direction within 0.05 m. In the vertical direction lidar and SfM are comparable within 0.20 m in unvegetated areas. This study further demonstrates the ability of the UAV/SfM workflow to map and monitor active mass-wasting processes in regions where landslides pose a threat to the surrounding community. Additionally, this thesis assesses the erosional characteristics of two recently burned areas in northern California: the 2015 Wragg Fire and the 2016 Emerald Fire. For the 2015 Wragg Fire, I compare observed post-fire erosion with USGS post-fire debris-flow models. For the 2016 Emerald Fire, I attempt estimate eroded material through multi-sourced DoDs and compare with field measurements. The aims of this study are to (1) further demonstrate the potential of UAV-SfM techniques in geomorphic studies and hazards management, (2) quantify landslide displacements and volumes by differencing multi-temporal DEMs and (3) document various mass-wasting/erosional processes across northern California. By increasing our understanding the various mass-wasting processes affecting northern California, we can help improve disaster preparation, response and management efforts potentially reducing damages and saving lives.

Download or read book Very High Resolution VHR Satellite Imagery written by Francisco Eugenio and published by MDPI. This book was released on 2019-11-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

Download or read book Modern Technologies for Landslide Monitoring and Prediction written by Marco Scaioni and published by Springer. This book was released on 2015-01-23 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Technologies for Landslide Investigation and Prediction presents eleven contributed chapters from Chinese and Italian authors, as a follow-up of a bilateral workshop held in Shanghai on September 2013. Chapters are organized in three main parts: ground-based monitoring techniques (photogrammetry, terrestrial laser scanning, ground-based InSAR, infrared thermography, and GNSS networks), geophysical (passive seismic sensor networks) and geotechnical methods (SPH and SLIDE), and satellite remote-sensing techniques (InSAR and optical images). Authors of these contributes are internationally-recognized experts in their respective research fields. Marco Scaioni works in the college of Surveying and Geo-Informatics at Tongji University, Shanghai (P.R. China). His research fields are mainly Close-range Photogrammetry, Terrestrial Laser Scanning, and other ground-based sensors for metrological and deformation monitoring applications to structural engineering and geosciences. In the period 2012-2016 he is chairman of the Working Group V/3 in the International Society for Photogrammetry and Remote Sensing, focusing on ‘Terrestrial 3D Imaging and Sensors’.

Download or read book Remote Sensing Imagery written by Florence Tupin and published by John Wiley & Sons. This book was released on 2014-02-19 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dedicated to remote sensing images, from their acquisition to their use in various applications, this book covers the global lifecycle of images, including sensors and acquisition systems, applications such as movement monitoring or data assimilation, and image and data processing. It is organized in three main parts. The first part presents technological information about remote sensing (choice of satellite orbit and sensors) and elements of physics related to sensing (optics and microwave propagation). The second part presents image processing algorithms and their specificities for radar or optical, multi and hyper-spectral images. The final part is devoted to applications: change detection and analysis of time series, elevation measurement, displacement measurement and data assimilation. Offering a comprehensive survey of the domain of remote sensing imagery with a multi-disciplinary approach, this book is suitable for graduate students and engineers, with backgrounds either in computer science and applied math (signal and image processing) or geo-physics. About the Authors Florence Tupin is Professor at Telecom ParisTech, France. Her research interests include remote sensing imagery, image analysis and interpretation, three-dimensional reconstruction, and synthetic aperture radar, especially for urban remote sensing applications. Jordi Inglada works at the Centre National d’Études Spatiales (French Space Agency), Toulouse, France, in the field of remote sensing image processing at the CESBIO laboratory. He is in charge of the development of image processing algorithms for the operational exploitation of Earth observation images, mainly in the field of multi-temporal image analysis for land use and cover change. Jean-Marie Nicolas is Professor at Telecom ParisTech in the Signal and Imaging department. His research interests include the modeling and processing of synthetic aperture radar images.

Download or read book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification written by Anil Kumar and published by CRC Press. This book was released on 2020-07-19 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Download or read book Spatial Modeling in GIS and R for Earth and Environmental Sciences written by Hamid Reza Pourghasemi and published by Elsevier. This book was released on 2019-01-18 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Download or read book Laser Scanning Applications in Landslide Assessment written by Biswajeet Pradhan and published by Springer. This book was released on 2017-05-04 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail. The book contains scanning data in tropical forests; its indicators, assessment, modeling and implementation. Additionally, debris flow modeling and analysis including source of debris flow identification and rockfall hazard assessment are also presented.

Download or read book Principles of Applied Remote Sensing written by Siamak Khorram and published by Springer. This book was released on 2016-01-04 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is one of the first to explain the fundamentals and applications of remote sensing at both undergraduate and graduate levels. Topics include definitions and a brief history of payloads and platforms, data acquisition and specifications, image processing techniques, data integration and spatial modeling, and a range of applications covering terrestrial, atmospheric, oceanographic and planetary disciplines. The policy and law issues of remote sensing and the future trends on the horizon are also covered. Remote sensing is an exciting, dynamic technology that is transforming the Earth sciences – terrestrial, atmospheric, and marine – as well as the practices of agriculture, disaster response, engineering, natural resources, providing evidence in legal cases and documented humanitarian crises, and many other fields. Increasingly, understanding of these techniques will be central to a number of disciplines, particularly as the technology advances.

Download or read book Remote Sensing in Precision Agriculture written by Salim Lamine and published by Elsevier. This book was released on 2023-10-20 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links

Download or read book Geospatial Technology for Earth Observation written by Deren Li and published by Springer Science & Business Media. This book was released on 2009-09-18 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Earth Observation interacts with space, remote sensing, communication, and information technologies, and plays an increasingly significant role in Earth related scientific studies, resource management, homeland security, topographic mapping, and development of a healthy, sustainable environment and community. Geospatial Technology for Earth Observation provides an in-depth and broad collection of recent progress in Earth observation. Contributed by leading experts in this field, the book covers satellite, airborne and ground remote sensing systems and system integration, sensor orientation, remote sensing physics, image classification and analysis, information extraction, geospatial service, and various application topics, including cadastral mapping, land use change evaluation, water environment monitoring, flood mapping, and decision making support. Geospatial Technology for Earth Observation serves as a valuable training source for researchers, developers, and practitioners in geospatial science and technology industry. It is also suitable as a reference book for upper level college students and graduate students in geospatial technology, geosciences, resource management, and informatics.