Earth observation data analytics using machine and deep learning modern tools, applications and challenges
"Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges" covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and...
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| Weitere Verfasser: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
2023
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| Schriftenreihe: | IET computing series
56 |
| Online-Zugang: | DE-91 DE-706 URL des Erstveröffentlichers |
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| Zusammenfassung: | "Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges" covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and multi-temporal observations. Approaches for applying pre-processing methods and deep learning techniques to satellite images for various applications - such as identifying land cover features, object detection, crop classification, target recognition, and the monitoring of earth resources - are described. Cost-efficient resource allocation solutions are provided, which are robust against common uncertainties that occur in annotating and extracting features on satellite images. This book is a valuable resource for engineers and researchers in academia and industry working on AI, machine and deep learning, data science, remote sensing, GIS, SAR, satellite communications, space science, image processing and computer vision. It will also be of interest to staff at research agencies, lecturers and advanced students in related fields. Readers will need a basic understanding of computing, remote sensing, GIS and image interpretation. |
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| Beschreibung: | 1 Online-Ressource (xvi, 240 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781839536182 |
| DOI: | 10.1049/PBPC056E |