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Hyperspectral Remote Sensing of Agriculture and Vegetation

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Hyperspectral Remote Sensing

Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging;

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Hyperspectral Remote Sensing

reliable detection and identification. Presenting the fundamentals of remote sensing at an introductory level, Hyperspectral Remote Sensing;

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Hyperspectral Remote Sensing

reliable detection and identification. Presenting the fundamentals of remote sensing at an introductory level, Hyperspectral Remote Sensing;

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Remote Sensing in the 21st Century

This volume covers almost all the operational applications and developments of remote sensing from agriculture and forestry to hydrology;

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Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests

of hyperspectral remote sensing for assessing ecosystem characteristics, Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests focuses on the;

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Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

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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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Signal and Image Processing for Remote Sensing

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the;

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Multi-sensor System Applications in the Everglades Ecosystem

in wetlands using data collected from airborne and space multispectralhyperspectral sensors, light detection and ranging (LiDAR), Unmanned Aircraft;

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Multi-sensor System Applications in the Everglades Ecosystem

in wetlands using data collected from airborne and space multispectralhyperspectral sensors, light detection and ranging (LiDAR), Unmanned Aircraft;

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Multitemporal Remote Sensing

in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data. This book;

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Remote Sensing Vegetation P

Remote sensing is becoming an increasingly important tool for agriculturalists, ecologists, and land managers for the study of Earth's;

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Environmental Remote Sensing and Systems Analysis

Remote sensing is the technique of gathering data and information about an object or phenomenon without making direct contact with the;

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Introduction to Remote Sensing

declassified CORONA imagery and other topics of current significance, plus new chapters on hyperspectral remote sensing and global remote sensing;

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Introduction To Remote Sensing

declassified CORONA imagery and other topics of current significance, plus new chapters on hyperspectral remote sensing and global remote sensing;

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Introduction To Remote Sensing

declassified CORONA imagery and other topics of current significance, plus new chapters on hyperspectral remote sensing and global remote sensing;

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Introduction To Remote Sensing

declassified CORONA imagery and other topics of current significance, plus new chapters on hyperspectral remote sensing and global remote sensing;

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Advances in Environmental Remote Sensing

shadow. Section III focuses on remote sensing of vegetation and related features of the Earth's surface. It reviews advancements in the;

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Exploring the Earth System with Imaging Spectroscopy

termed hyperspectral remote sensing, for terrestrial Earth observation dates back to the 1980s, when the first spectrometers observing in the;

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Kernel Methods for Remote Sensing Data Analysis

now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods;

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Remote Sensing of Vegetation

How is the vegetation distribution influencing the erosion and surface formation in the different eco zones of Chile? To answer this;

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Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

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Remote Sensing

Remote sensing uses sensors or satellites to acquire information about objects. Doppler radar, LIDAR, radio meters, hyperspectral imaging;

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Remote Sensing

Remote sensing uses sensors or satellites to acquire information about objects. Doppler radar, LIDAR, radio meters, hyperspectral imaging;

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Deep Learning for Hyperspectral Image Analysis and Classification

. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions;

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Deep Learning for Hyperspectral Image Analysis and Classification

. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions;

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