Object Based Image Analysis PDF Download
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| Author | : Thomas Blaschke |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 804 |
| Release | : 2008-08-09 |
| Genre | : Science |
| ISBN | : 3540770585 |
Download Object-Based Image Analysis Book in PDF, ePub and Kindle
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).
| Author | : Sven Nussbaum |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 178 |
| Release | : 2008-01-09 |
| Genre | : Science |
| ISBN | : 1402069618 |
Download Object-Based Image Analysis and Treaty Verification Book in PDF, ePub and Kindle
This book describes recent progress in object-based image interpretation. It presents new results in its application to verification of nuclear non-proliferation. A comprehensive workflow and newly developed algorithms for object-based high resolution image (pre-) processing, feature extraction, change detection, classification and interpretation are developed, applied and evaluated. The analysis chain is demonstrated with satellite imagery acquired over Iranian nuclear facilities.
| Author | : Kumar Navulur |
| Publisher | : CRC Press |
| Total Pages | : 206 |
| Release | : 2006-12-05 |
| Genre | : Technology & Engineering |
| ISBN | : 1420043072 |
Download Multispectral Image Analysis Using the Object-Oriented Paradigm Book in PDF, ePub and Kindle
Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery. This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving. Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.
| Author | : Russell G. Congalton |
| Publisher | : CRC Press |
| Total Pages | : 210 |
| Release | : 2008-12-12 |
| Genre | : Mathematics |
| ISBN | : 1420055135 |
Download Assessing the Accuracy of Remotely Sensed Data Book in PDF, ePub and Kindle
Accuracy assessment of maps derived from remotely sensed data has continued to grow since the first edition of this groundbreaking book. As a result, the much-anticipated new edition is significantly expanded and enhanced to reflect growth in the field. The new edition features three new chapters, including: Fuzzy accuracy assessmentPositional accu
| Author | : Aboul Ella Hassanien |
| Publisher | : Springer |
| Total Pages | : 474 |
| Release | : 2018-12-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3030023575 |
Download Machine Learning Paradigms: Theory and Application Book in PDF, ePub and Kindle
The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
| Author | : Boguslaw Cyganek |
| Publisher | : John Wiley & Sons |
| Total Pages | : 518 |
| Release | : 2013-05-20 |
| Genre | : Science |
| ISBN | : 111861836X |
Download Object Detection and Recognition in Digital Images Book in PDF, ePub and Kindle
Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.
| Author | : BENNETT |
| Publisher | : McGraw Hill |
| Total Pages | : 713 |
| Release | : 2010-04-16 |
| Genre | : Computers |
| ISBN | : 0077139712 |
Download Ebook: Object-Oriented Systems Analysis and Design Using UML Book in PDF, ePub and Kindle
Ebook: Object-Oriented Systems Analysis and Design Using UML
| Author | : Yuhong He |
| Publisher | : CRC Press |
| Total Pages | : 417 |
| Release | : 2018-06-27 |
| Genre | : Technology & Engineering |
| ISBN | : 0429892993 |
Download High Spatial Resolution Remote Sensing Book in PDF, ePub and Kindle
High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation. To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions: What are the challenges of using new sensors and new platforms? What are the cutting-edge methods for fine-level information extraction from high spatial resolution images? How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes? The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.
| Author | : Steven M. de Jong |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 370 |
| Release | : 2007-07-26 |
| Genre | : Science |
| ISBN | : 1402025602 |
Download Remote Sensing Image Analysis: Including the Spatial Domain Book in PDF, ePub and Kindle
Remote Sensing image analysis is mostly done using only spectral information on a pixel by pixel basis. Information captured in neighbouring cells, or information about patterns surrounding the pixel of interest often provides useful supplementary information. This book presents a wide range of innovative and advanced image processing methods for including spatial information, captured by neighbouring pixels in remotely sensed images, to improve image interpretation or image classification. Presented methods include different types of variogram analysis, various methods for texture quantification, smart kernel operators, pattern recognition techniques, image segmentation methods, sub-pixel methods, wavelets and advanced spectral mixture analysis techniques. Apart from explaining the working methods in detail a wide range of applications is presented covering land cover and land use mapping, environmental applications such as heavy metal pollution, urban mapping and geological applications to detect hydrocarbon seeps. The book is meant for professionals, PhD students and graduates who use remote sensing image analysis, image interpretation and image classification in their work related to disciplines such as geography, geology, botany, ecology, forestry, cartography, soil science, engineering and urban and regional planning.
| Author | : Valliappa Lakshmanan |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 481 |
| Release | : 2021-07-21 |
| Genre | : Computers |
| ISBN | : 1098102339 |
Download Practical Machine Learning for Computer Vision Book in PDF, ePub and Kindle
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
