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Full Description
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.
Contents
Chapter 1: Introduction
Part I: Clustering and classification of Earth Observation data
Chapter 2: Deep learning method for crop classification using remote sensing data
Chapter 3: Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cover estimation
Chapter 4: Leveraging twin networks for land use land cover classification
Chapter 5: Exploiting artificial immune networks for enhancing RS image classification
Chapter 6: Detection and segmentation of aircrafts in UAV images with a deep learning-based approach
Part II: Rare event detection using Earth Observation data
Chapter 7: A transfer learning approach for hurricane damage assessment using satellite imagery
Chapter 8: Wildfires, volcanoes and climate change monitoring from satellite images using deep neural networks
Chapter 9: A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study of an Indian state
Chapter 10: Machine learning paradigm for predicting reservoir property: an exploratory analysis
Part III: Tools and technologies for Earth Observation data
Chapter 11: The application of R software in water science
Chapter 12: Geospatial big data analysis using neural networks
Chapter 13: Software framework for spatiotemporal data analysis and mining of earth observation data
Chapter 14: Conclusion
