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Multi-sensor and multi-temporal remote sensing, Kumar, Anil (indian Institute Of Remote Sensing, Dehradun, India) Upadhyay, Priyadarshi (uttarakhand Space Application Centre, Dehradun, India) Singh,


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Автор: Kumar, Anil (indian Institute Of Remote Sensing, Dehradun, India) Upadhyay, Priyadarshi (uttarakhand Space Application Centre, Dehradun, India) Singh,
Название:  Multi-sensor and multi-temporal remote sensing
ISBN: 9781032428321
Издательство: Taylor&Francis
Классификация:









ISBN-10: 1032428325
Обложка/Формат: Hardback
Страницы: 148
Вес: 0.39 кг.
Дата издания: 17.04.2023
Иллюстрации: 19 tables, black and white; 22 line drawings, black and white; 48 halftones, black and white; 70 illustrations, black and white
Размер: 240 x 164 x 16
Подзаголовок: Specific single class mapping
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Поставляется из: Европейский союз
Описание: This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields.Key features:Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classesDiscusses range of fuzzy/deep learning models capable to extract specific single class and separates noiseDescribes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a classSupports multi-sensor and multi-temporal data processing through in-house SMIC softwareIncludes case studies and practical applications for single class mappingThis book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.
Дополнительное описание: 1. Remote-Sensing Images 2. Evolution of Pixel-Based Spectral Indices 3. Multi-Sensor, Multi-Temporal Remote-Sensing 4. Training Approaches—Role of Training Data 5. Machine-Learning Models for Specific-Class Mapping 6. Learning-Based Algorithms for Specif



Physical Principles of Remote Sensing (2017)

Автор: Rees
Название: Physical Principles of Remote Sensing (2017)
ISBN: 052118116X ISBN-13(EAN): 9780521181167
Издательство: Cambridge Academ
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Цена: 6599.00 р.
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Описание: Covering a wide range of remote sensing techniques and applications, this new edition is now more accessible to students, while retaining its focus on physical and mathematical principles. Chapter summaries, review questions, problem sets and supporting online material allow students to test their understanding and practise handling data for themselves.

Multi-Step Multi-Input One-Way Quantum Information Processing with Spatial and Temporal Modes of Light

Автор: Ryuji Ukai
Название: Multi-Step Multi-Input One-Way Quantum Information Processing with Spatial and Temporal Modes of Light
ISBN: 4431550186 ISBN-13(EAN): 9784431550181
Издательство: Springer
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Цена: 18284.00 р.
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Описание: In this thesis, the author develops for the first time an implementation methodology for arbitrary Gaussian operations using temporal-mode cluster states. The experimental basic structure of one-way quantum computation over two-mode input state is demonstrated by the controlled-Z gate and the optimum nonlocal gate experiments.

Multi-Sensor Data Fusion: An Introduction

Автор: H.B. Mitchell
Название: Multi-Sensor Data Fusion: An Introduction
ISBN: 3642090672 ISBN-13(EAN): 9783642090677
Издательство: Springer
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Цена: 13059.00 р.
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Описание: This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.

Distributed Heterogeneous Multi Sensor Task Allocation Systems

Автор: Tkach Itshak, Edan Yael
Название: Distributed Heterogeneous Multi Sensor Task Allocation Systems
ISBN: 3030347370 ISBN-13(EAN): 9783030347376
Издательство: Springer
Цена: 13974.00 р.
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Описание: Introduction.- Multi-agent task allocation.- Multi-Sensor task allocation systems.- Evaluation methodology.- Single-layer multi-sensor task allocation system.- Extended examples of single-layer multi-sensor systems.- Dual-layer multi-sensor task allocation system.- Extended example of dual-layer multi-sensor task allocation systems.- Fault tolerant multi sensor system with high availability.- Analytical analysis of a simplified scenario of two sensors and two tasks.

Remote Sensing for Food Security

Автор: Kogan
Название: Remote Sensing for Food Security
ISBN: 3319962558 ISBN-13(EAN): 9783319962559
Издательство: Springer
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Цена: 22359.00 р.
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Описание: This volume gathers a variety of applications for remote sensing of vegetation health (VH) and concretely shows how this information can be used in service of ending hunger and of ensuring future food security.

Spatio-temporal Approaches - Geographic Objects and Change Process

Автор: Mathian
Название: Spatio-temporal Approaches - Geographic Objects and Change Process
ISBN: 1848215525 ISBN-13(EAN): 9781848215528
Издательство: Wiley
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Цена: 22010.00 р.
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Описание: Spatio-temporal Approaches presents a well-built set of concepts, methods and approaches, in order to represent and understand the evolution of social and environmental phenomena within the space.

Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

Название: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation
ISBN: 1138058548 ISBN-13(EAN): 9781138058545
Издательство: Taylor&Francis
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Цена: 23734.00 р.
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Описание: This book discusses both the strengths and the limitations of the topics covered: (a) hyperspectral processes, (b) sensors, and (c) data analysis. Each chapter reviews existing "state-of-art" knowledge, highlights the advances made, and provides guidance for appropriate use of hyperspectral data in study of vegetation and its numerous applications.

Sensor Application in Managing in-Season Crop Variability

Автор: Solange Uwera
Название: Sensor Application in Managing in-Season Crop Variability
ISBN: 1774690381 ISBN-13(EAN): 9781774690383
Издательство: Mare Nostrum (Eurospan)
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Цена: 20691.00 р.
Наличие на складе: Нет в наличии.

Описание: It is essential to produce climate system models that can bring about an accurate representation of the climate forecast. The concept of global warming has become a local phrase for every region and its effects are obvious on the farming communities. The models generated can be used to project the current situations and can help us predict or forecast the potential perils in the future. In this book, the sensor applications are explained with respect to the agricultural sectors. Remote sensing and GIS are considered as important tools in mapping agricultural fields and their associated factors. The mapping of crops with several space-borne platforms and airborne platforms is explained. This book can be used as a handbook for engineers, scientists and research scholars along with undergraduates and postgraduates in science and technology.

Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

Автор: Thenkabail, Prasad S.
Название: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation
ISBN: 1032475889 ISBN-13(EAN): 9781032475882
Издательство: Taylor&Francis
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Цена: 7195.00 р.
Наличие на складе: Нет в наличии.

Spectral Analysis for Univariate Time Series

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 14573.00 р.
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.

Multi-component torque sensing systems

Автор: Liang, Qiaokang
Название: Multi-component torque sensing systems
ISBN: 0367502402 ISBN-13(EAN): 9780367502409
Издательство: Taylor&Francis
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Цена: 7961.00 р.
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Описание: This book will focus on the design, development, decoupling, and applications of multi-component torque sensing systems. Force and moment information can be used as feedback to form an automatic control system to accomplish efficient manipulation.

Multi-Component Force Sensing Systems

Автор: Liang, Qiaokang
Название: Multi-Component Force Sensing Systems
ISBN: 0367511258 ISBN-13(EAN): 9780367511258
Издательство: Taylor&Francis
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Цена: 3367.00 р.
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