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| Author | : D.R. Welikanna |
| Publisher | : |
| Total Pages | : 57 |
| Release | : 2008 |
| Genre | : |
| ISBN | : |
| Author | : Scott Baum |
| Publisher | : Springer Nature |
| Total Pages | : 207 |
| Release | : 2021-06-05 |
| Genre | : Science |
| ISBN | : 9811616779 |
This book highlights major quantitative and qualitative methods and approaches used in the field of urban analysis. The respective chapters cover the background and relevance of various approaches to urban studies and offer guidance on implementing specific methodologies. Each chapter also provides links to real-world examples. The book is unique in its focus on Australian examples and subject matter, presented by recognized experts in the field.
| Author | : Havard Rue |
| Publisher | : Chapman and Hall/CRC |
| Total Pages | : 280 |
| Release | : 2005-02-18 |
| Genre | : Mathematics |
| ISBN | : 9781584884323 |
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studies and, online, a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.
| Author | : Yoshiki Yamagata |
| Publisher | : Academic Press |
| Total Pages | : 302 |
| Release | : 2019-11-03 |
| Genre | : Business & Economics |
| ISBN | : 0128131322 |
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics
| Author | : Saurabh Prasad |
| Publisher | : Springer Nature |
| Total Pages | : 464 |
| Release | : 2020-04-27 |
| Genre | : Computers |
| ISBN | : 3030386171 |
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
| Author | : Nareenart Raksuntorn |
| Publisher | : |
| Total Pages | : |
| Release | : 2009 |
| Genre | : Algorithms |
| ISBN | : |
The objective of this dissertation is to investigate all the necessary components in spectral mixture analysis (SMA) for hyperspectral imagery under an unsupervised circumstance. When SMA is linear, referred to as linear spectral mixture analysis (LSMA), these components include estimation of the number of endmembers, extraction of endmember signatures, and calculation of endmember abundances that can automatically satisfy the sum-to-one and non-negativity constraints. A simple approach for nonlinear spectral mixture analysis (NLSMA) is also investigated. After SMA is completed, a color display is generated to present endmember distribution in the image scene. It is expected that this research will result in an analytic system that can yield optimal or suboptimal SMA output without prior information. Specifically, the uniqueness in each component is described as follow. 1) A new signal subspace-based approach is developed to determine the number of endmembers with relatively robust performance and the least parameter requirement. 2) The best implementation strategy is determined for endmember extraction algorithms using simplex volume maximization and pixel spectral similarity; and algorithm with the special consideration for anomalous pixels is developed to improve the quality of extracted endmembers. 3) A new linear mixture model (LMM) is deployed where the number of endmembers and their types can be changed from pixel to pixel such that the resulting endmember abundances are sum-to-one and nonnegative as required; and fast algorithms are developed to search for a sub-optimal endmember set for each pixel. 4) A simple approach for NLSMA based on LMM is investigated and an automated approach is developed to determine either linear or nonlinear mixing is actually experienced. 5) A color display strategy is developed to present SMA results with high class/endmember separability.
| Author | : |
| Publisher | : |
| Total Pages | : 76 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
In this thesis, two methods for the Maximum A Posteriori (MAP) based super-resolution are proposed. All the degradation parameters, namely, additive noise, blur and sub-pixel motion are considered unknown. The study focuses on the simultaneous estimation of the unknown parameters and the underlying high-resolution image. Two types of image priors have been considered, the Gaussian Simultaneous Autoregressive (SAR) and the Huber Markov Random Field (HMRF), and the results have been compared. Special focus is laid on the estimation of the PSF blurring and two methods have been proposed for the modeling of PSF blur and its estimation. Mathematical derivations and analytical proofs support the algorithms. Conclusions are drawn on the basis of the performance evaluation of the proposed algorithms with each other and with the existing techniques. It is shown that the two methods proposed achieve stable and desired solution for the super-resolution problem.
| Author | : Thomas Blaschke |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 804 |
| Release | : 2008-08-09 |
| Genre | : Science |
| ISBN | : 3540770585 |
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 | : Ruslan K. Chornei |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 286 |
| Release | : 2006-02-21 |
| Genre | : Mathematics |
| ISBN | : 9780387304090 |
This book is devoted to the study and optimization of spatiotemporal stochastic processes - processes which develop simultaneously in space and time under random influences. These processes are seen to occur almost everywhere when studying the global behavior of complex systems. The book presents problems and content not considered in other books on controlled Markov processes, especially regarding controlled Markov fields on graphs.
| Author | : Weixing Zhang |
| Publisher | : |
| Total Pages | : 112 |
| Release | : 2018 |
| Genre | : Cities and towns |
| ISBN | : |
The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet.
