Multitemporal Earth Observation Image Analysis

Clément Mallet

ISBN 10: 1789451760 / ISBN 13: 9781789451764

Published by John Wiley & Sons Aug 2024, 2024

About this Item

Neuware - Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.

Seller Inventory # 9781789451764

Bibliographic Details

• Title: Multitemporal Earth Observation Image Analysis
• Publisher: John Wiley & Sons Aug 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 1789451760
• ISBN 13: 9781789451764
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 156H x 16D millimeters 234 millimeters width by 156 millimeters height by 16 millimeters depth
• Weight: 558 grams

About this title

Synopsis

Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies.

Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives.

The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.

About the Author

Clément Mallet is a senior scientist in remote sensing for land-cover mapping issues at the LaSTIG Laboratory (Gustave Eiffel University, IGN, French Mapping Agency), France. He is also Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing.

Nesrine Chehata is a senior lecturer in geomatics and AI for Earth observation at ENSEGID-Bordeaux INP, France. She is also President and Co-founder of AGEOS (African Association for Geospatial Development), President of FNAACC (National Forum of Climate Change Adaptation Actors in Tunisia) and an IEEE GRSS senior member.