Multi Sensor And Multi Temporal Remote Sensing
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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.
Remote Sensing of Drought
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 through
Analysis Of Multi-temporal Remote Sensing Images - Proceedings Of The First International Workshop On Multitemp 2001
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.