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Computer Processing of Remotely-Sensed Images A thorough introduction to computer processing of remotely-sensed images, processing methods;
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detection, and impacts Includes R computer codes with explanatory comments and all applications use only freely available remotely sensed;
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R computer codes with explanatory comments and all applications use only freely available remotely sensed data Presents a step-by-step;
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remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, * New;
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presents practical quantitative algorithms for estimating various land surface variables from remotely sensed observations. A concise review of the;
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of Landsat images were performed with the Hadoop framework. The findings demonstrate that MapReduce has a potential for scaling large-scale remotely;
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Remotely sensed data, in the form of digital images captured from spaceborne and airborne platforms, provide a rich analytical and;
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algorithms such as face recognition and signature verification; Remotely sensed images and their applications; Principles and applications of dynamic;
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location of the remotely sensed data in real time.;
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A volume in the Remote Sensing Handbook series, Remotely Sensed Data Characterization, Classification, and Accuracies documents the;
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goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple;
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goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple;
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temporal requirements of remotely sensed data in relation to various urban phenomena; methods and techniques for analyzing and integrating remotely;
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The fusion of remote sensed images can integrate the spectral information of single sensor or the information from different kinds;
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Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures is een boek;
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and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse;
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Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has;
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. Illustrated with remotely sensed color images from satellites and aircraft, it also outlines data acquisition and analysis. New to this edition;
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Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth;
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and 1970's. As a tool to improve environmental co-operation, remote sensing has great promise. Just as remotely sensed images raised awareness;
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processing algorithms help connect and enhance the collective understanding of engineering design and remotely sensed data;
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form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book;
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networks, as a new form of computational paradigm, seem well suited to many of the tasks involved in remotely sensed image analysis. This book;
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This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also;
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of remotely sensed data and analyses have tended to discourage scientists and managers from using this valuable resource. This text focuses on making;
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populations and economic activity, particularly on a global scale. Remotely Sensed Cities helps to redress the balance with remote sensing books;
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remotely sensed data GIS is predominantly a data-handling technology, while remote sensing is a data retrieval and analysis technology. This;
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