Multi-Sensor and Multi-Temporal Remote Sensing PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Multi-Sensor and Multi-Temporal Remote Sensing PDF full book. Access full book title Multi-Sensor and Multi-Temporal Remote Sensing by Anil Kumar. Download full books in PDF and EPUB format.
Author: Anil Kumar Publisher: CRC Press ISBN: 100087219X Category : Computers Languages : en Pages : 178
Book Description
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 classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes 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 class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This 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.
Author: Anil Kumar Publisher: CRC Press ISBN: 100087219X Category : Computers Languages : en Pages : 178
Book Description
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 classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes 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 class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This 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.
Author: Yifang Ban Publisher: Springer ISBN: 331947037X Category : Technology & Engineering Languages : en Pages : 448
Book Description
Written by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse-resolution time series processing, and interferometric SAR multitemporal processing, among others. A broad range of multitemporal datasets are used in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data. This book features a variety of application examples covering both land and aquatic environments. Land applications include urban, agriculture, habitat disturbance, vegetation dynamics, soil moisture, land surface albedo, land surface temperature, glacier and disaster recovery. Aquatic applications include monitoring water quality, water surface areas and water fluctuation in wetland areas, spatial distribution patterns and temporal fluctuation trends of global land surface water, as well as evaluation of water quality in several coastal and marine environments. This book will help scientists, practitioners, students gain a greater understanding of how multitemporal remote sensing could be effectively used to monitor our changing planet at local, regional, and global scales.
Author: Paul Smits Publisher: World Scientific ISBN: 981448234X Category : Technology & Engineering Languages : en Pages : 404
Book Description
The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the coming years. Its importance and timeliness are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere at different scales. However, the advances in the methodologies for the analysis of multi-temporal data have been significantly under-illuminated with respect to other remote sensing data analysis topics. In addition, the link between the end-users' needs and the scientific community needs to be strengthened. This volume of proceedings contains 43 contributions from researchers representing academia, industry and governmental organizations. It is organized into three thematic sections: Image Analysis and Algorithms; Analysis of Synthetic Aperture Radar Data; Monitoring and Management of Resources. Contents:Image Analysis and Algorithms:Extending Time-Series of Satellite Images by Radiometric Intercalibration (A Röder et al.)Trajectory of Dynamic Clusters in Image Time Series (P Heas et al.)Change Detection with ALI and Landsat Satellite Data (H Chen et al.)Analysis of Synthetic Aperture Radar Data:Multi-Temporal Interferometric Point Target Analysis (U Wegmüller et al.)Application of Multiple Baseline InSAR Data for DEM Generation (S Takeuchi)Joint Distributions for Multi-Temporal Series of Radar Images (B Storvik et al.)Monitoring and Management of Resources:Detection of Vegetation Changes in an Alpine Protected Area (M Maggi et al.)Monitoring Drought Stress in North-Eastern China by Means of Rainfall Data and Diachrone Indices Derived from Pathfinder AVHRR-Imagery (P Ozer et al.)Science for Society: Global Observations of Earth's Natural Resources in the 21st Century (R L King)and other papers Readership: Graduate students and researchers in computer science and environmental science. Keywords:Remote Sensing;Change Detection;Multi-Temporal Image Analysis;Pattern Recognition;Time Series Analysis;Environmental Monitoring;Environmental Management;Natural Resources;Earth Observation
Author: Lorenzo Bruzzone Publisher: World Scientific ISBN: 9812702636 Category : Computers Languages : en Pages : 403
Book Description
The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the coming years. Its importance and timeliness are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth''s surface and atmosphere at different scales. However, the advances in the methodologies for the analysis of multi-temporal data have been significantly under-illuminated with respect to other remote sensing data analysis topics. In addition, the link between the end-users'' needs and the scientific community needs to be strengthened.This volume of proceedings contains 43 contributions from researchers representing academia, industry and governmental organizations. It is organized into three thematic sections: Image Analysis and Algorithms; Analysis of Synthetic Aperture Radar Data; Monitoring and Management of Resources.
Author: Christine Pohl Publisher: CRC Press ISBN: 1315353067 Category : Technology & Engineering Languages : en Pages : 283
Book Description
Remote Sensing Image Fusion: A Practical Guide gives an introduction to remote sensing image fusion providing an overview on the sensors and applications. It describes data selection, application requirements and the choice of a suitable image fusion technique. It comprises a diverse selection of successful image fusion cases that are relevant to other users and other areas of interest around the world.?The book helps newcomers to obtain a quick start into the practical value and benefits of multi-sensor image fusion. Experts will find this book useful to obtain an overview on the state of the art and understand current constraints that need to be solved in future research efforts. For industry professionals the book can be a great introduction and basis to understand multisensor remote sensing image exploitation and the development of commercialized image fusion software from a practical perspective. The book concludes with a chapter on current trends and future developments in remote sensing image fusion. Along with the book, RSIF website provides additional up-to-date information in the field.
Author: Gary Jedlovec Publisher: IntechOpen ISBN: 9789533070056 Category : Technology & Engineering Languages : en Pages : 754
Book Description
Remote sensing is the acquisition of information of an object or phenomenon, by the use of either recording or real-time sensing device(s), that is not in physical or intimate contact with the object (such as by way of aircraft, spacecraft, satellite, buoy, or ship). In practice, remote sensing is the stand-off collection through the use of a variety of devices for gathering information on a given object or area. Human existence is dependent on our ability to understand, utilize, manage and maintain the environment we live in - Geoscience is the science that seeks to achieve these goals. This book is a collection of contributions from world-class scientists, engineers and educators engaged in the fields of geoscience and remote sensing.
Author: Lorenzo Bruzzone Publisher: World Scientific ISBN: 9812777245 Category : Computers Languages : en Pages : 455
Book Description
The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the next few years. The relevance and timeliness of this issue are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere.This book brings together the methodological aspects of multi-temporal remote sensing image analysis, real applications and end-user requirements, presenting the state of the art in this field and contributing to the definition of common research priorities. Researchers and graduate students in the fields of remote sensing, image analysis, and environmental monitoring will appreciate the interdisciplinary approach thanks to the articles written by experts from different scientific communities.
Author: Claudia Kuenzer Publisher: Springer ISBN: 3319159674 Category : Technology & Engineering Languages : en Pages : 441
Book Description
This volume comprises an outstanding variety of chapters on Earth Observation based time series analyses, undertaken to reveal past and current land surface dynamics for large areas. What exactly are time series of Earth Observation data? Which sensors are available to generate real time series? How can they be processed to reveal their valuable hidden information? Which challenges are encountered on the way and which pre-processing is needed? And last but not least: which processes can be observed? How are large regions of our planet changing over time and which dynamics and trends are visible? These and many other questions are answered within this book “Remote Sensing Time Series Analyses – Revealing Land Surface Dynamics”. Internationally renowned experts from Europe, the USA and China present their exciting findings based on the exploitation of satellite data archives from well-known sensors such as AVHRR, MODIS, Landsat, ENVISAT, ERS and METOP amongst others. Selected review and methods chapters provide a good overview over time series processing and the recent advances in the optical and radar domain. A fine selection of application chapters addresses multi-class land cover and land use change at national to continental scale, the derivation of patterns of vegetation phenology, biomass assessments, investigations on snow cover duration and recent dynamics, as well as urban sprawl observed over time.
Author: Lorenzo Bruzzone Publisher: World Scientific ISBN: 981448833X Category : Technology & Engineering Languages : en Pages : 456
Book Description
The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the next few years. The relevance and timeliness of this issue are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere. This book brings together the methodological aspects of multi-temporal remote sensing image analysis, real applications and end-user requirements, presenting the state of the art in this field and contributing to the definition of common research priorities. Researchers and graduate students in the fields of remote sensing, image analysis, and environmental monitoring will appreciate the interdisciplinary approach thanks to the articles written by experts from different scientific communities. Contents:Image Analysis and Algorithms:Digital Change Detection Methods in Natural Ecosystem Monitoring: A Review (P Coppin et al.)Adaptive Reduction of Registration-Noise Effects in Unsupervised Change Detection (L Bruzzone & R Cossu)Image Thresholding for Landslide Detection by Genetic Programming (P L Rosin & J Hervás)Shape Change Detection by Fuzzy Measures (P C Smits & S G Dellepiane)The Use of SOM-Encoded Texture Spectrum for Change Detection (J C W Chan & K-P Chan)A Temporal Extension to Traditional Empirical Orthogonal Function Analysis (A A Nielsen et al.)Monitoring and Management of Natural Resources:Using Temporal Change of the Land Cover Spectral Signal to Improve Burnt Area Mapping (D Stroppiana & J-M Grégoire)MODIS 250m and 500m Time Series Data for Change Detection and Continuous Representation of Vegetation Characteristics (J R G Townshend et al.)Suspended Matter in the North Sea: A Multi-Temporal Analysis based on SEAWIFS Imagery in 2000 (R Pasterkamp et al.)The Use of Multi-Temporal SAR Images for Detecting Main Hydrological Parameters (M Bianchini et al.)Land-Cover Dynamics:Multi-Temporal Remote Sensing Analytical Approaches for Characterizing Landscape Change (R S Lunetta)An Application of Kalman Filtering for Monitoring Forest Growth Aided by Satellite Image Time Series (S Joyce)Fourier Decomposition of an AVHRR NDVI Time Series for Seasonal and Interannual Land Cover Change Detection (M E Jakubauskas et al.)Multi-temporal Analysis Using NOAA-pathfinder AVHRR Land Imagery for the Study of the Land Cover Dynamic in the Mediterranean Basin (J A Sobrino et al.)and other papers Readership: Researchers and academics in computer science, computer engineering, electrical and electronic engineering, and environmental science. Keywords:
Author: Anil Kumar
Author: Anil Kumar
Author: Yifang Ban
Author: Paul Smits
Author: Lorenzo Bruzzone
Author: Christine Pohl
Author: Gary Jedlovec
Author: Lorenzo Bruzzone
Author: Claudia Kuenzer
Author: Paul Smits
Author: Lorenzo Bruzzone