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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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Landcover Classification and Topography

Landcover mapping is an important tool for natural resource managers. The incorporation of remotely sensed data to produce landcover maps;

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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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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

This book provides a step-by-step guide on how to use various publicly available remotely sensed time series data sources for environmental;

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

This book provides a step-by-step guide on how to use various publicly available remotely sensed time series data sources for environmental;

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Computer Processing of Remotely-Sensed Images 5e

provides students with methods of computer processing of remotely sensed data and introduces them to environmental applications which make use;

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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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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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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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Multispectral Image Analysis Using the Object-Oriented Paradigm

for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and;

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Multispectral Image Analysis Using the Object-Oriented Paradigm

for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and;

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Neurocomputation in Remote Sensing Data Analysis

This volume gives a state of the art view of recent developments in the use of artificial neural networks for the analysis of remotely;

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Remote Sensing for Ecology and Conservation

remotely sensed data can be captured and used, especially for evaluating human impacts on ecological systems.;

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Uncertainty Management in Remote Sensing of Climate Data

Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a;

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Groundwater Recharge and Flow: Approaches and Challenges for Monitoring and Modeling Using Remotely Sensed Data

requires remotely sensed data and observations applied to groundwater models. Resolving the interaction of groundwater storage, flow, and recharge;

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Assessing the Accuracy of Remotely Sensed Data

The past 10 years have brought amazing changes to the technologies used to turn remotely sensed data into maps. As a result, the principles;

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Assessing the Accuracy of Remotely Sensed Data

The past 10 years have brought amazing changes to the technologies used to turn remotely sensed data into maps. As a result, the principles;

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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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Remotely-Sensed Cities

New urban applications are emerging for remote sensing, in particular with the use of high-resolution data for measuring, monitoring and;

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Urban High-Resolution Remote Sensing

and algorithms focused on three very important roots of urban remote sensing: 3D urban modelling using different remotely sensed data, urban;

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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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Image Analysis, Classification and Change Detection in Remote Sensing

development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it;

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Image Analysis, Classification and Change Detection in Remote Sensing

development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it;

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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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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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Measuring the Oceans from Space: The Principles and Methods of Satellite Oceanography

, including some of the global applications of the data. The variety of applications of remotely sensed data to ocean science has grown significantly;

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Advances in Water Science Methodologies

requirements for the spatial, temporal and spectral resolutions of remotely sensed data. To reflect this diversity of needs, this book considers a;

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