Multi Sensor And Multi Temporal Remote Sensing

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.

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.

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.

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.

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.

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 importance 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 environmental monitoring, remote sensing image analysis and pattern recognition will appreciate the interdisciplinary approach thanks to the articles written by experts from different scientific communities.

Remote Sensing of Drought: Innovative Monitoring Approaches presents emerging remote sensing-based tools and techniques that can be applied to operational drought monitoring and early warning around the world. The first book to focus on remote sensing and drought monitoring, it brings together a wealth of information that has been scattered throughout the literature and across many disciplines. Featuring contributions by leading scientists, it assembles a cross-section of globally applicable techniques that are currently operational or have potential to be operational in the near future. The book explores a range of applications for monitoring four critical components of the hydrological cycle related to drought: vegetation health, evapotranspiration, soil moisture and groundwater, and precipitation. These applications use remotely sensed optical, thermal, microwave, radar, and gravity data from instruments such as AMSR-E, GOES, GRACE, MERIS, MODIS, and Landsat and implement several advanced modeling and data assimilation techniques. Examples show how to integrate this information into routine drought products. The book also examines the role of satellite remote sensing within traditional drought monitoring, as well as current challenges and future prospects. Improving drought monitoring is becoming increasingly important in addressing a wide range of societal issues, from food security and water scarcity to human health, ecosystem services, and energy production. This unique book surveys innovative remote sensing approaches to provide you with new perspectives on large-area drought monitoring and early warning.

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.