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Computer Processing of Remotely-Sensed Images 5e

Computer Processing of Remotely-Sensed Images A thorough introduction to computer processing of remotely-sensed images, processing methods;

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

detection, and impacts Includes R computer codes with explanatory comments and all applications use only freely available remotely sensed;

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

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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Computer Processing Of Remotely-Sensed Images

remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, * New;

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Quantitative Remote Sensing of Land Surfaces

presents practical quantitative algorithms for estimating various land surface variables from remotely sensed observations. A concise review of the;

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Distributed Processing of Large Remote Sensing Images Using Mapreduce

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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Image Processing and Data Analysis with ERDAS IMAGINE (R)

Remotely sensed data, in the form of digital images captured from spaceborne and airborne platforms, provide a rich analytical and;

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Image Processing

algorithms such as face recognition and signature verification; Remotely sensed images and their applications; Principles and applications of dynamic;

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Real-Time Auto Geotagging and Visualization of Remotely Sensed Data

location of the remotely sensed data in real time.;

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Remotely Sensed Data Characterization, Classification, and Accuracies

A volume in the Remote Sensing Handbook series, Remotely Sensed Data Characterization, Classification, and Accuracies documents the;

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Remote Sensing Image Fusion

goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple;

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Remote Sensing Image Fusion

goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple;

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Remote Sensing of Urban and Suburban Areas

temporal requirements of remotely sensed data in relation to various urban phenomena; methods and techniques for analyzing and integrating remotely;

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Image Fusion Techniques for Remote Sensed Data

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

Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures is een boek;

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Multitemporal Remote Sensing

and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse;

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Classification Methods for Remotely Sensed Data

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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Introduction to Remote Sensing

. 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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Introductory Digital Image Processing

Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth;

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Application of Space Technology with Fitting of Engineering Management for Pollution Monitoring

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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Remote Sensing

processing algorithms help connect and enhance the collective understanding of engineering design and remotely sensed data;

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Signal and Image Processing for Remote Sensing

form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book;

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Neurocomputation in Remote Sensing Data Analysis

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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Remote Sensing Image Classification in R

This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also;

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Remote Sensing for Ecology and Conservation

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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Remotely-Sensed Cities

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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Remote Sensing and GIS Integration

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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