Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges" />Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges" />
Affiliations: 1: Computer Science and Engineering Department, Jaypee University of Engineering and Technology, India
2: Computer Science and Engineering Department, Institute of Technology, Nirma University, India
3: Planetary and Meteorology Data Processing Group, Space Applications Centre (SAC), ISRO, India
4: Land and Municipal Service, Dholera Industrial City Development Limited (DICDL), India
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.
